Image sequences recorded with cameras mounted in a moving vehicle provide information
about the vehicle’s environment which has to be analysed in order to really support the driver
in actual traffic situations. One type of information is the lane structure surrounding the vehicle.
Therefore, driver assistance functions which make explicit use of the lane structure represented
by lane borders and lane markings is to be analysed. Lane analysis is performed on the road
region to remove road pixels. Only lane markings are the interests for the lane detection
process. Once the lane boundaries are located, the possible edge pixels are scanned to
continuously obtain the lane model. The developed system can reduce the complexity of vision
data processing and meet the real time requirements.
Vehicle Detection using Camera
Vehicle Detection Using Cameras for Self-Driving Cars |
Using machine learning and computer vision I create a pipeline that detects nearby vehicles from a dash-cam.
Vehicle Detection using Camera
Vehicle Detection Using Cameras for Self-Driving Cars |
Using machine learning and computer vision I create a pipeline that detects nearby vehicles from a dash-cam.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Google Self Driving Cars
The Google Self-Driving Car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car". The project is currently being led by Google engineer Sebastian Thrun, former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense. The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.
Legislation has been passed in four states and the District of Columbia allowing driverless cars. The U.S. state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws. The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads, and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Videos
https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
Smart infrastructure for autonomous vehicles Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how autonomous vehicles are becoming economic feasible. They are becoming economically feasible because the cost of lasers, ICs, MEMS, and other electronic components are falling at 25 to 40% per year. If the cost of autonomous vehicles fall 25% a year, the cost of the electronics associated with autonomous vehicles will fall 90% in 10 years. Dedicating roads to autonomous vehicles is necessary to achieve the most benefits from autonomous vehicles. While using autonomous vehicles in combination with conventional vehicles can free drivers for other activities, dedicating roads to autonomous vehicles can dramatically reduce congestion, increase speeds, and thus increase the number of cars per area of the road. They can also reduce accidents, insurance, and the number of traffic police. These slide discuss the use of wireless technologies for the control and coordination of autonomous vehicles. Improvements in bandwidth, speed, and latency (delays) along with improvements in computer processing are occurring and these improvements are making dedicated roads for autonomous vehicles economically feasible.
A Method for Predicting Vehicles Motion Based on Road Scene Reconstruction an...ITIIIndustries
The suggested method helps predicting vehicles movement in order to give the driver more time to react and avoid collisions on roads. The algorithm is dynamically modelling the road scene around the vehicle based on the data from the onboard camera. All moving objects are monitored and represented by the dynamic model on a 2D map. After analyzing every object’s movement, the algorithm predicts its possible behavior.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Google Self Driving Cars
The Google Self-Driving Car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur. Lettering on the side of each car identifies it as a "self-driving car". The project is currently being led by Google engineer Sebastian Thrun, former director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense. The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.
Legislation has been passed in four states and the District of Columbia allowing driverless cars. The U.S. state of Nevada passed a law on June 29, 2011, permitting the operation of autonomous cars in Nevada, after Google had been lobbying in that state for robotic car laws. The Nevada law went into effect on March 1, 2012, and the Nevada Department of Motor Vehicles issued the first license for an autonomous car in May 2012, to a Toyota Prius modified with Google's experimental driverless technology. In April 2012, Florida became the second state to allow the testing of autonomous cars on public roads, and California became the third when Governor Jerry Brown signed the bill into law at Google HQ in Mountain View. In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.
Videos
https://www.youtube.com/channel/UCCLyNDhxwpqNe3UeEmGHl8g
Smart infrastructure for autonomous vehicles Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how autonomous vehicles are becoming economic feasible. They are becoming economically feasible because the cost of lasers, ICs, MEMS, and other electronic components are falling at 25 to 40% per year. If the cost of autonomous vehicles fall 25% a year, the cost of the electronics associated with autonomous vehicles will fall 90% in 10 years. Dedicating roads to autonomous vehicles is necessary to achieve the most benefits from autonomous vehicles. While using autonomous vehicles in combination with conventional vehicles can free drivers for other activities, dedicating roads to autonomous vehicles can dramatically reduce congestion, increase speeds, and thus increase the number of cars per area of the road. They can also reduce accidents, insurance, and the number of traffic police. These slide discuss the use of wireless technologies for the control and coordination of autonomous vehicles. Improvements in bandwidth, speed, and latency (delays) along with improvements in computer processing are occurring and these improvements are making dedicated roads for autonomous vehicles economically feasible.
A Method for Predicting Vehicles Motion Based on Road Scene Reconstruction an...ITIIIndustries
The suggested method helps predicting vehicles movement in order to give the driver more time to react and avoid collisions on roads. The algorithm is dynamically modelling the road scene around the vehicle based on the data from the onboard camera. All moving objects are monitored and represented by the dynamic model on a 2D map. After analyzing every object’s movement, the algorithm predicts its possible behavior.
A Much Advanced and Efficient Lane Detection Algorithm for Intelligent Highwa...cscpconf
This paper presents a much advanced and efficient lane detection algorithm. The algorithm is based on (ROI) Region of Interest segmentation. In this algorithm images are pre-processed by top-hat transform for de-noising and enhancing contrast. ROI of a test image is then extracted. For detecting lines in the ROI, Hough transform is used. Estimation of the distance between Hough origin and lane-line midpoint is made. Lane departure decision is made based on the difference between these distances. As for the simulation part we have used Matlab software.Experiments show that the proposed algorithm can detect the lane markings accurately and quickly.
Online/Offline Lane Change Events Detection AlgorithmsFeras Tanan
Abstract—in this paper, We are presenting two algorithms
for lane change detection. The first one is used
for online detection (real-time detection) with accuracy of
85% and the other one is used for offline detection with
accuracy of 95%. The main purpose of the offline detection
algorithm is to find at which GPS locations the number
of happened left/right lane changes.
For the purpose of these algorithms we used the
”crowd-sensing” approach which means that the sensors of
different mobile devices that were fixed in different cars
are the sources of input data for the above mentioned
algorithms. Specifically speaking, we used Accelerometer
and Gyroscope sensors. We also presented an algorithm
for blinker pattern extraction using the microphone sensor.
Keywords: Pattern Extraction, Lane Change detection,
Accelerometer, Gyroscope and Crowd Sensing
REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...ijcsit
Lane detection and tracking is one of the key features of advanced driver assistance system. Lane detection is finding the white markings on a dark road. Lane tracking use the previously detected lane markers and adjusts itself according to the motion model. In this paper, review of lane detection and tracking algorithms developed in the last decade is discussed. Several modalities are considered for lane detection which
include vision, LIDAR, vehicle odometry information,information from global positioning system and digital maps. The lane detection and tracking is one of the challenging problems in computer vision.Different vision based lane detection techniques are explained in the paper. The performance of different lane detection and tracking algorithms is also compared and studied.
A VISION-BASED REAL-TIME ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM USING VEHICULA...JANAK TRIVEDI
In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.
Traffic flow measurement for smart traffic light system designTELKOMNIKA JOURNAL
Determining congestions on intersection roads can significantly improve the performance of a traffic light system. One of the everyday problems on our roads nowadays is the unbalanced traffic on different roads. The blind view of roads and the dependency on the conventional timer-based traffic light systems can cause unnecessary delays on some arterial roads on expense of offering a needless extra pass time on some other secondary minor roads. In this paper, a foreground extraction model has been built in MATLAB platform to measure the congestions on the different roads constructing an intersection. Results show a satisfactory performance in terms of accuracy in counting cars and in consequence reducing the wait time on some major roads. System was tested under different weather and lighting conditions, and results were adequately promising.
A computer vision-based lane detection technique using gradient threshold and...IJECEIAES
Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.
Vehicle detection and tracking techniques a concise reviewsipij
Vehicle detection and tracking applications play an important role for civilian and military applications
such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection
process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic
analysis and vehicle categorizing objectives and may be implemented under different environments
changes. In this review, we present a concise overview of image processing methods and analysis tools
which used in building these previous mentioned applications that involved developing traffic surveillance
systems. More precisely and in contrast with other reviews, we classified the processing methods under
three categories for more clarification to explain the traffic systems.
A computer vision-based lane detection approach for an autonomous vehicle usi...Md. Faishal Rahaman
Lane detection systems play a critical role in ensuring safe and secure driving by alerting the driver of lane departures. Lane detection may also save passengers' lives if they go off the road owing to driver distraction. The article presents a three-step approach for detecting lanes on high-speed video pictures in real-time and invariant lighting. The first phase involves doing appropriate prepossessing, such as noise reduction, RGB to grey-scale conversion, and binarizing the input picture. Then, a polygon area in front of the vehicle is picked as the zone of interest to accelerate processing. Finally, the edge detection technique is used to acquire the image's edges in the area of interest, and the Hough transform is used to identify lanes on both sides of the vehicle. The suggested approach was implemented using the IROADS database as a data source. The recommended method is effective in various daylight circumstances, including sunny, snowy, and rainy days, as well as inside tunnels. The proposed approach processes frame on average in 28 milliseconds and have a detection accuracy of 96.78 per cent, as shown by implementation results. This article aims to provide a simple technique for identifying road lines on high-speed video pictures utilizing the edge feature.
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
2. 346 Computer Science & Information Technology (CS & IT)
and other image artifacts. Certain class of lane detection methods [3, 4] relies on top-view (birds
eye) images computed from images acquired by the camera. These methods are reliable in
obtaining lane orientation in world coordinates, but require online computation of the top-view
images (and camera calibration). Deformable road models have been widely used for lane
detection and tracking [5–9].Such techniques rely on mathematical models to fit road boundaries.
In general, simpler models (e.g. linear) do not provide an accurate fit, but they are more robust
with respect to image artifacts. On the other hand, more complex models (such as parabolic and
splines) are more flexible, but also more sensitive to image artifacts/noise. Driver assistant system
based on computer vision is helpful in relieving the contradiction between enhancement of traffic
safety and increment of traffic density. The system can gather information on the environment
surrounding the vehicle and detect dangerous conditions. Lane departure detection module is an
important part in driver assistance systems and it is used to control the vehicle’s lateral position
on the road. The location and heading of the vehicle to current lane can be obtained from
comparison of steering by the driver and one estimated by an intelligent lateral control system.
For lateral control, the most crucial variables to be sensed are the position and orientation of the
vehicle relative to the road boundary. In addition to these parameters, sensing of road curvature is
quite significant as it facilitates a smooth trajectory. The approaches used to solve this problem
exploit the information on road boundaries or lane markings, in which the lateral control depends
on lane following and changing. This work focuses on development of vision based lane
departure detection.In order to support a driver in a manner which is perceived as unobtrusive and
helpful rather than distracting a model-driven approach helps in the determination of the actual
traffic situation surrounding the driver.
Various kinds of models are necessary:
1) a model of the behaviour of a vehicle on the road,
2) a model of the overall public road system,
3) a model of a generic geometry of the road visible in front of the vehicle,
4) models for vehicles observable on the road,
5) control-theoretic models for the realization of a complete set of driving maneuvers,
6) a model for how such maneuvers are to be serialized under normal traffic conditions.
With increasing computing power of standard PCs it is possible to realize more complex driver
assistance with general purpose hardware as well as to implement more sophisticated algorithms.
Two different algorithms are developed to extract measurement points in the image of not only
marked but unmarked lane borders as well. Different road types as well as various traffic
situations and illumination changes require great care on robustness and reliability. Obstacle
information can be used by the system to increase robustness. The algorithm can be extended to
track two adjacent lanes. Additionally, classification of marked lane border types based on the
already detected measurement points of the lane tracking algorithm can lead to a higher level
representation of the road which helps to understand the environmental conditions of the actual
situation. Based on this robust video-based lane detection algorithm, lane keeping assistance
system can be developed which warns the driver on unintended lane departures. A number of
assumptions on driver behaviour in certain situations can be integrated to distinguish between
intended and unintended lane departures. The driver assistance functions are based on the
detection and tracking of road borders and road markings which is described in the subsequent
sections.
2. Literature Survey
Various approaches for lane detection systems have been performed. Dickmanns and Mysliwetz
[6] performed a recursive 3-D road and relative vehicle’s motion behaviour recognition using four
3. Computer Science & Information Technology (CS & IT) 347
Intel 80286 and one 386 microprocessors. On the other hand, Leblanc et al.[7] created an
automatic road-departure warning system called CAPC by using two Kalman filters to estimate
vehicle state and both local and previewed road geometry information. Besides, Kreucher and
Lakshmanan [8] presented a LANA system to extract lane markings in frequency domain and to
detect lane with a deformable template. Bertozzi and Broggi[3] presented a GOLD system using
stereo inverse perspective mapping. Kaszubiak et al.[10] used two CMOS cameras to measure the
disparity map locating road objects and detect the lane position using a Hough transform. Wu et
al.[11] presented an adaptive lane departure warning system operating in frequency 600-MHz
processor by detecting lane markings. Yeh and Chen[12] developed a vision-based lane and
vehicle detecting system by enhancing lane markings and locate left-right boundaries. Jeng et
al.[13] presented a real-time mobile lane detection system using generic 2-D Gaussian smoothing
filter and global edge detection. Pankiewicz et al[14] performed a simple canny detector and
linear Hough Transform to locate lane boundaries. Apostoloff and Zelinsky proposed a lane
tracking system based on particle filtering and multiple cues. In fact, this method does not track
explicitly the lanes, but it computes parameters such as lateral offset and yaw of the vehicle with
respect to the center of the road. Although the method appears to be robust under a variety of
conditions (shadows, different lighting conditions, etc.), it cannot be used to estimate curvature or
detect if the vehicle is approaching a curved part of the road. McCall and Trivedi proposed a
method for lane detection using steerable filters. Such filters perform well in picking out both
circular reflector road markings as well as painted line road markings. Filter results are then
processed to eliminate outliers based on the expected road geometry and used to update a road
and vehicular model along with data taken internally from the vehicle. Such technique is robust
with respect to lighting changes and shadows, but has shortcomings for relatively curved roads
(because this method relies basically on a linear model). LeBlancetal. [7] proposed a road-
departure prevention system, that predicts the vehicles’s path and compares such path with the
sensed road geometry to estimate the time to lane-crossing (TLC). However, the vision-based
sensor requires good lighting and pavement conditions to detect lane boundaries. Risacketal
proposed a lane keeping assistance system, which warns the driver on unintended lane departures.
Infact, they used an existing video based lane detection algorithm and compared different
methods to detect lane departure, using several assumptions on driver behaviour in certain
situations to distinguish between intended and unintended lane departures. Lane departures are
successfully detected, by their technique, but they also needed roads in good conditions and
lighting conditions. Lee proposed a lane departure detection system that estimates lane
orientation through an edge distribution function (EDF), and identifies changes in the travelling
direction of a vehicle. However, the EDF may fail in curved roads with dashed lane markings. A
modification of this technique [15] includes a boundary pixel extractor to improve its robustness.
However, curved lanes may still cause problems, because a linear model (computed using the
Hough transform) is used for fitting lane boundaries. The AURORA system applied a
binormalized adjustable template correlation technique using downward looking cameras.
However, it worked well only for slowly-varying roads.
3. Methodology
3.1. System design
The system consists of four subsystems: the sensor (videocamera), image processing, the
controller and the vehicle as shown in Fig1.
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Fig.1.The four subsystems of a vision-based autonomous vehicle driving control system.
3.1.1. Sensor
The sensor is the key element of an autonomous vehicle system, because it provides the
information about a driving scenario. The system discussed here uses a single video camera as a
sensor. To get the input data from the image, the video image sequences must be captured. The
input data of this system is provided by colour image sequences taken from a moving vehicle. A
single colour video camera is mounted inside the vehicle behind the wind shield along the central
line. This records the images of the environment in front of the vehicle, including the road, the
vehicles on the road, traffic signs on the roadside and, sometimes, incidents on the road. The
video camera saves the video images in AVI file format, then the video file is transferred to the
computer. The image processing subsystem takes an image from the memory and starts
processing it in order to detect the desired lane.
3.1.2. Image Processing and Analysis for Predicting and Detecting the vehicle lane
The goal of the image processing is to extract information about the position of the vehicle with
respect to the road from the video image. Two major processes are implemented: the pre-
processing process and then the lane detection process. The goal of pre-processing is to remove
image noise and make the images sharper. The goal of lane detection is to detect the desired lane
of the vehicle in order to obtain the look-ahead distance and the lane angle. This process is based
on the real-time data of video sequences taken from a vehicle driving on the road. The four
processing steps of the lane detection algorithm are image segmentation, edge detection, Hough
Transform, and lane tracking as shown in Fig.2.
5. Computer Science & Information Technology (CS & IT) 349
Fig. 2. Image processing steps of the lane detection algorithm.
3.1.3. Regions of interest measurement and features extraction using Colour Cue
segmentation
Segmentation of the images is crucial for the analysis of the driving environment. Parts of the
traffic scenes can be recovered by extracting geometric features in order to infer and verify the
existence of certain categories of objects. Another relevant method of segmentation is based on
region growing and clustering. The effectiveness of such a method crucially depends upon the
capability of measuring similarity, such as in texture and pixel colour information. A colour-
based visual module provides relevant information for localization of the visible road area,
independently of the presence of lane boundary markings and in different lighting conditions. In
the road image, the road area has characteristics such as the following: 1) most of the lower part
of the image was considered as the road area, and 2) road areas have a quasi-uniform colour,
resulting from the fact that the road area is generally a grey surface in a more coloured
environment. Although the absolute surface colour can provide useful cues for this task, the
response of the colour imaging device is mediated by the colour of the surfaces observed, by the
colour of light illuminating them, and by the setting of the image acquisition system. To have
better control over variations in pixel values for the same colour, and to remove the shadows, the
RGB colour space must be converted to the HSV (hue, saturation and value) colour space. Fig.3.
shows the image by image segmentation process by computing the coordinates of regions of
interest and determining the pixel colour of interest. Fig. 4. shows the lane marking extraction
based on the colour of pixels of interest.
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Fig. 3. Road surface as the object/region of interest. Other
objects/background are converted to black (0).
Fig. 4. Lane marking extraction based on the colour of pixels of interest.
3.1.4. Extraction and detection of vehicle lane edges using the edge detectors
The purpose of the edge detection process is to extract the image edges using an edge detection
operator or an edge detector. The operator will locate the position of pixels where significant
pixels exist. The edges are represented as white and non- edges will be black. Fig.5. shows the
lane edges of the image. Edges in images are areas with strong intensity contrasts. Edge detecting
of an image significantly reduces the amount of data and filters out useless information while
preserving the important structural properties of the image. Edge detection is the most common
approach for detecting meaningful discontinuities in the grey level. Intuitively, an edge is a set of
connected pixels that lie on the boundary between two regions. It is important that edges
occurring in an image should not be missed and that there is no response to non-edges. The
second constituent is that the edge points are well localized. In other words, the distance between
the edge pixels as detected by the detector and the actual edge is to be minimal. A third is that
there is only one response to a single edge.
7. Computer Science & Information Technology (CS & IT) 351
Fig. 5. Edges of lane marking and some unwanted edges.
3.1.5. Features isolation and approximation of the vehicle lane using Hough Transform
Hough Transform was used to combine edges into lines, where a sequence of edge pixels in a line
indicates that a real edge exists. By using the edge data of the road image, Hough Transform will
detect the lane boundary on the image. This is because this technique detects shapes from image
edges, and assumes that primitive edge detection has already been performed on an image. This
technique is most useful when detecting boundaries whose shape can be described in an analytical
or tabular form. The key function of this system is to map a line detection problem into a simple
peak detection problem in the space of the parameters of the line. Hough Transform is a technique
that can be used to isolate features of a particular shape within an image. It can be divided into
two types: classical and generalized. Because it requires the desired features to be specified in
some parametric form, the classical Hough Transform is most commonly used for detection of
regular curves, such as lines, circles, ellipses, etc. A generalized Hough Transform can be
employed in applications where a simple analytic description of a feature is not possible. Hough
Transform works by letting each feature point (x, y) vote in (m, b) space for each possible line
passing through it. These votes are totalled in an accumulator. If, for instance, a particular (m, b)
has one vote, this means that there is a feature point through which this line passes. If it has two
votes, it means that two feature points lie on that line. If a position (m, b) in the accumulator has n
votes, this means that n feature points lie on that line. The algorithm for the Hough Transform can
be expressed as follows:
1. Find all of the desired feature points in the image.
2. For each feature point: For each possibility i in the accumulator that passes through the feature
point, increment that position in the accumulator.
3. Find local maximum in the accumulator.
4. If desired, map each maximum in the accumulator back to the image space.
3.1.6. Lane tracking
A distinction can be made between the problems of lane detection and tracking. Lane detection
involves determining the location of the lane boundaries in a single image without strong prior
knowledge regarding the lane position. On the other hand, lane tracking involves determining the
location of the lane boundaries in a sequence of consecutive images, using information about the
lane location from previous images in the sequence to constrain the probable lane detection in the
8. 352 Computer Science & Information Technology (CS & IT)
current image. In each second, the first several image frames will be processed by the lane
detection algorithm, and this will provide a good estimate of lane tracking for the next frames.
Kalman filter is used to predict the model parameters.
4. Camera Calibration
The system detects lane markings using a monochromatic CCD camera mounted behind the
windshield. Preferred position of the camera is behind the rear view mirror in order to get a clear
view through the windshield as shown in figure 6. The first frame acquired by the camera is
processed, and the two (left and right) lane boundaries are obtained automatically. Our coordinate
system coincides with image coordinates, and a threshold xm separates the near and far vision
fields. The choice for xm depends on the size of the acquired images and the tilt angle of the
camera, and should result in a length of about10m for the near field (in a typical camera
installation with resolution of 240 by 320pixels, it is possible to see about 30 – 40 m ahead with a
reasonable definition).
Fig.6. Experimental set up .
5. Initial lane detection
For the initial detection, a linear model for the lane boundary is chosen, because of its simplicity
and robustness. We also assume that the following conditions are satisfied in the first frame of the
video sequence:
a) the vehicle is initially located in a straight portion of the road;
b) the vehicle is approximately aligned with the road;
c)there are no significant linear structures in the image, except for the lane boundaries.
To detect the linear lane boundaries, EDF approach is adopted combined with the Hough
transform.
5.1. The edge distribution function
For the greyscale image I(x,y), the gradient function del I(x,y) can be approximated by:
9. Computer Science & Information Technology (CS & IT) 353
where Dx and Dy are differences computed in the x and y directions (this differences can be
computed using the Sobel operator ).We can estimate the gradient magnitude and orientation
using the following equations:
To determine the orientation of the road boundaries, we compute the edge distribution function
(EDF), which is the histogram of the gradient magnitude with respect to the orientation. To
compute this histogram, the angles θ(x, y) within the range [-90, +90] were quantized in 90
subintervals. Considering that lanes are the only significant linear objects in the image, and that
the car is aligned with the central axis of the road, it is expected that lane boundaries will generate
two local maxima in the EDF. However, multi-lane roads generate several local maxima. Infact,
the figure illustrates a road with three traveling lanes (and the vehicle is located in the middle
lane). Fig.7. illustrates the EDF for this image, and four local maxima can be observed. The first
one (at θ= -36) is related to the right boundary of the central lane; the second one (at θ= -14) is
related to the right boundary of the right lane; the third one (at θ= 12) is related to the left
boundary of the left lane; finally, the last one (at θ= 28) is related to the left boundary of the
central lane.
In general, inner lane boundaries (where the car is travelling) have approximately symmetric
orientations and are closer to vertical lines in the image (i.e. correspond to smallest and largest
values of θ in the EDF). If α1 and α 2 denote the smallest and largest orientations of the EDF,
respectively, then they are kept if
α1 + α2 < T1
where T1 is a threshold. Let α be the orientation corresponding to the desired lane boundary.
Also, let g(x, y) be the directional edge image. It should be noticed that g(x, y) contains edge
magnitudes of the original image I(x, y) that are aligned with the direction α. These magnitudes
will be mostly related to the lane boundary, but some pixels related to noise or other structures
that are aligned with the lane may also appear. Fig.8 shows image g(x, y) for α = -36 , which
corresponds to the right lane boundary. Indeed, some isolated pixels with small magnitude that
are not related to this lane boundary appear in the image.
10. 354 Computer Science & Information Technology (CS & IT)
Fig.7. Smoothed edge distribution function.
Fig.8. Magnitudes aligned with the right lane boundary.
Applying the Hough transform to a set of edge points (xi,yi) results in a 2Dfunction C(ρ, θ) that
represents the number of edge points satisfying the linear equation ρ = xcosθ + ysinθ. In practical
applications, the angles θ and distances r are quantized, and we obtain an array C(ρ, θ). The local
maxima of C(ρ, θ) can be used to detect straight line segments passing through edge points. In our
case, the orientation θ can be obtained from the EDF peak.
This line detection procedure is applied independently to each lane boundary, resulting in one
linear model for each boundary. This initial detection is used to find the lane boundary region of
interest (LBROI), which will be the search space for lane boundaries in the subsequent frames of
the video sequence. The LBROI is obtained by ‘thickening’ the detected lane boundary ,such that
it is extended w(x) pixels to the right and w(x) pixels to the left in the y direction. Due to camera
perspective, LBROIs should be thinner at the top of the image (farfield), and fatter at the
bottom(nearfield). A simple choice for w(x) is a linear function, stretching w bottom pixels to both
sides at the bottom and wtop pixels at the top. The LBROIs shown in Fig. 9.
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Fig.9. LBROIs corresponding to initial lane segmentation using the linear model.
6. Estimation of Additional Lanes
In order to extract more information from each image, the two lanes adjacent to the
current(main)lane are estimated as well. The same model and the same measuring method as for
the main lane is applied to perform this task. Some parameters of the neighbour lanes are assumed
to be identical with the main lane, as the yaw angle and the curvature parameters. These
parameters are not estimated for the neighbour lanes. The pitch angle is used to check the
plausibility of the neighbour lanes. If the pitch angle difference between a neighbour lane and the
main lane exceeds a threshold, the neighbour lane estimation is rejected.
7. Classification of Marking Lines
Marked and unmarked lane borders are distinguished during the detection of measurement points.
A marked lane border can appear differently, e.g., as solid or as dashed marking line. Solid and
dashed marking lines can be distinguished by analyzing gaps between the measurement points.
The measurement points are sorted with ascending corresponding distance in world coordinates.
When a solid marking line is tracked, there will be measurement points on almost every scan line.
On dashed marking lines, some scan lines will have no measurement point. The gap size between
two successive measurement points is computed in world coordinates by the difference of the
look ahead distances corresponding to the measurement points. If the maximal gap size in the
world for one lane border exceeds a threshold, the border is classified as a dashed marking line,
otherwise it is classified as a solid line.
8. Situation Analysis
It is important to clearly determine when a warning should be issued. Therefore, situations are to
be defined which are parameterized with the lane geometry and the driving maneuver. There are
three possible lane geometries: “straight,” “leftbend,” and “rightbend.” Each of them can be
combined with the driving maneuvers “keeping the lane,” “leaving the lane to the left,” and
“leaving the lane to the right” resulting in certain basic situations our warning system has to cope
with.
In the situation “keeping the lane,” clearly, no warning should be given, regardless of the form of
the lane. In the cases “leaving the lane,” warnings should be issued only when the lane is left
unintentionally. It is necessary to monitor the behaviour of the driver to detect intentional lane
leavings. Situations with intentional leaving are :
12. 356 Computer Science & Information Technology (CS & IT)
1) lane changes,
2) corner cuts,
3)evasive maneuvers in emergency cases.
Warnings in these situations are regarded as false warnings (falsepositive). To achieve a high user
acceptance of the system, no false positives are allowed. Important states of the car which enable
to monitor the driver intentions are
1) blinker state(off, left, right),
2) braking,
3) steering angle.
These information can be easily obtained from a car network, e.g. aCAN-bus.
8.1. Intended Leaving
To avoid false warnings during intended lane departures, additional information is necessary.
Consider the following intended departures:
1) lane changes which are announced by the driver using the blinker,
2) emergency maneuvers with brake and high steering activity,
Before changing a lane, drivers are obliged to set the blinker to the according direction. So, if the
blinker is set while departing the lane, warnings are suppressed. If the driver brakes he already
reacts to some situation and is most likely aware of the situation. Additional warnings would be
disturbing in such cases and are, therefore, suppressed. The same argument holds if there is a high
steering activity. Drivers who cut corners surely will not accept a system that warns in such
situations.
9. Vehicle’s Current Position.
The easiest way to detect the departure of the lane is to check the car’s current position(CCP)in
the lane. The position is estimated by the lane detection algorithm. The lateral offset y0 denotes
the distance between the center of the lane and the center of the car. Since the gear angle is small,
the car is approximately parallel to the lane. With the given car width bc, the position of the front
wheels relative to the lane borders can be computed by the equation
∆y = b – (y0 + bc )
2 2
∆y = b + (y0 - bc )
2 2
The current lane width b is also estimated by the lane detection algorithm. The upper and lower
line of the equation correspond to the position of the right and left wheel relative to the right and
left lane border, respectively. The car is inside a lane when both front wheels of the car are still
inside the lane. This is the case when ∆y > 0 in both cases. No warnings are necessary here. As
soon as one wheel crosses the lane border on its side, the car leaves the lane, then, ∆y < 0 on the
corresponding side of the lane. Whether a warning is necessary or not depends on the driver’s
intention.
13. Computer Science & Information Technology (CS & IT) 357
10. Departure estimation
If the vehicle is traveling in a straight portion of the road and stays at the center of the lane, we
should expect symmetry (for the near vision field) in the orientations of left and right lane
boundaries ,as depicted in Fig.10.If the vehicle drifts to its left, both θl and θr increase. If the
vehicle drifts to its right, both θl and θr decrease. In any case, the value of magnitude of θl plus θr
gets away from zero. Thus, a simple and efficient measure for trajectory deviation is given by:
β = | θl + θr |
If β gets sufficiently large, the vehicle is leaving the center of the lane. In this work, β is
compared to a threshold T3, and a lane departure warning is issued if β >T3. Experimental results
indicate that T3=15 degrees is a good choice, coinciding with threshold T1 used in the initial lane
detection algorithm.
Fig.10. Orientation of lane boundaries.
11. Feature extraction
In lane hypothesis generation step, lane-mark edge features like edge-orientation, edge-length,
and edge-pair width are checked to filter out noise edges and select candidate lane-mark edges.
For lane hypothesis verification, lane-mark colors are checked inside regions enclosed by
candidate lane-mark edge-pairs.
Generally there are three kinds of lane-mark colors: white, yellow and blue. These three colors
are much easier to identify in the YUV color space compared to the commonly used RGB color
space. Therefore, the color checking step is done in the YUV color space. The first thing is to
transform the RGB color space into the YUV color space using the equation,
Y 0.299 0.587 0.114 R
U = -0.169 -0.331 0.500 G
V 0.500 - 0.419 -0.081 B
Then the yellow-checking image is produced by U subtract by V, the blue-checking image is
generated by V subtract by U. White is checked by using the Y channel. The result is shown in
14. 358 Computer Science & Information Technology (CS & IT)
the Fig.11. By using adaptive thresholds, yellow, blue, and white dominant regions can be
extracted from the corresponding color checking channels.
Fig.11. Channels used for color checking.
13. Limitations
The main limitation of the proposed technique is regarded to significant occlusions of lane
markings due to vehicles in front of the camera as shown in fig.12. In fact, significant occlusions
occur only when the vehicle in front is very close to the camera (lessthan10m), which typically
happens only in traffic jam situations. Although the linear-parabolic model performs well in the
presence of sparse shadows (such as irregular shadows cast by trees), it may present erroneous
results if strong aligned shadows appear close to lane markings. In such cases, edges introduced
by shadows may be stronger than edges related to lane markings, specially in dashed markings.
(a) (b)
Fig.12. Limitations of the proposed technique. (a) Occlusion of lane markings. (b) Strong shadow causing
an erroneous detection of lane boundaries.
15. Computer Science & Information Technology (CS & IT) 359
14. CONCLUSIONS
In this paper, a robust lane detection method has been developed which utilize the human visual
properties of lateral inhibition, far-near adaptation, and joined the mutual support in feature
extraction. This approach for safe lane systems has developed a safety system for avoiding lane
departures for a large and complex set of traffic scenarios. Vehicles with complete lane keeping
support systems can be introduced with the help of active steering. Other driver assistance
applications such as lateral cruise control and collision avoidance can also be included along with
lane keeping support systems. According to the experimental results with the proposed methods
in the paper, we can summarize the following points:
(1) This system can be used under most of environments in the daylight, night time, sunny and
raining day.
(2) This system can be used under various lane-markings and vehicles for lane boundary
recognition and preceding vehicle detection.
(3) In various environments, the system can provide high availability, reliability and accuracy in
lane deviation and headway distance estimation.
(4) The image-processing rate of the system is more than 20 fps (frame per second), and it meets
the requirements of real-time computing in an embedded system.
Based on a single CMOS camera mounted on the windscreen, the system can recognize lane
boundary and preceding vehicle by means of image processing and provide the lane departure
warning and forward collision warning functions. In lane departure detection, gray scale statistics,
dynamic range of interesting (ROI) and featured-based approaches are applied to recognize the
lane boundaries and road geometry model is used to detect the lane departure. In addition, the
driver assistance system has taken convenience installation into consideration. By simplifying the
installation steps, the system can be adaptive to most of vehicles.
15. ACKNOWLEDGEMENTS
We would like to express our sincere gratitude to Dr. T.N. Padmanabhan Nambiar, Head of
Department, Electrical and Electronics Engineering for providing us affectionate guidance.
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