This document summarizes a project on real-time object detection using computer vision techniques. It discusses using a system that can recognize objects in a video stream from a camera and label them with bounding boxes and labels. It notes that most video surveillance footage is uninteresting unless there are moving objects. The project aims to address this by building an accurate, fast object detection system that can run on resource-constrained devices. It proposes using a hybrid CNN-SVM model trained on a large dataset to recognize objects and discusses the training and detection phases of the system.
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)cscpconf
The proposed work aims to create a smart application camera, with the intention of eliminating the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen Faces and the test images are verified by using distance based algorithm against the eigenfaces, like Euclidean distance algorithm or Mahalanobis Algorithm. If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an alarm signal is raised.
Traffic Violation Detector using Object Detection that helps to detects the vehicle number plate that is violating traffic rules and by that number the admin finds the details of the car owner and send a penalty charge sheet to the owner home.
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)cscpconf
The proposed work aims to create a smart application camera, with the intention of eliminating the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen Faces and the test images are verified by using distance based algorithm against the eigenfaces, like Euclidean distance algorithm or Mahalanobis Algorithm. If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an alarm signal is raised.
Traffic Violation Detector using Object Detection that helps to detects the vehicle number plate that is violating traffic rules and by that number the admin finds the details of the car owner and send a penalty charge sheet to the owner home.
Deep learning fundamental and Research project on IBM POWER9 system from NUSGanesan Narayanasamy
Moving object recognition (MOR) corresponds to the localisation and classification of moving objects in videos. Discriminating moving objects from static objects and background in videos is an essential task for many computer vision applications. MOR has widespread applications in intelligent visual surveillance, intrusion detection, anomaly detection and monitoring, industrial sites monitoring, detection-based tracking, autonomous vehicles, etc. In this session, Murari is going to talk about the deep learning algorithms to identify both locations and corresponding categories of moving objects with a convolutional network. The challenges in developing such algorithms will be discussed. The discourse will also include the implementation details of these models in both conventional and UAV videos.
Development of wearable object detection system & blind stick for visuall...Arkadev Kundu
It is a wearable device. It has a camera, and it detects all living and non living object. This module detects moving object also. It is made with raspberry pi 3, and a camera. One headphone connect with raspberry pi. When this module detects items, it gave a sound output through headphone. Hence the blind man know that item, which is in-front of him or her. We made it in very low budget, and it is very helpful for visually challenged people. And the Blind stick help him to detect obstacles.
Deep learning fundamental and Research project on IBM POWER9 system from NUSGanesan Narayanasamy
Moving object recognition (MOR) corresponds to the localisation and classification of moving objects in videos. Discriminating moving objects from static objects and background in videos is an essential task for many computer vision applications. MOR has widespread applications in intelligent visual surveillance, intrusion detection, anomaly detection and monitoring, industrial sites monitoring, detection-based tracking, autonomous vehicles, etc. In this session, Murari is going to talk about the deep learning algorithms to identify both locations and corresponding categories of moving objects with a convolutional network. The challenges in developing such algorithms will be discussed. The discourse will also include the implementation details of these models in both conventional and UAV videos.
Development of wearable object detection system & blind stick for visuall...Arkadev Kundu
It is a wearable device. It has a camera, and it detects all living and non living object. This module detects moving object also. It is made with raspberry pi 3, and a camera. One headphone connect with raspberry pi. When this module detects items, it gave a sound output through headphone. Hence the blind man know that item, which is in-front of him or her. We made it in very low budget, and it is very helpful for visually challenged people. And the Blind stick help him to detect obstacles.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Halogenation process of chemical process industries
slide-171212080528.pptx
1. REAL TIME OBJECT DETECTION
Presented by:-
Pratik Pratyay(12150052)
Project Guide:-
Dr. Latha R Nair
2. • Our goal is to design a system that runs real-time object detection.Formally, given a
continuous camera live stream we want to successfully recognize(draw bounding box and
label the object) the moving object in a small amount of time.
3. • Monitoring cameras are used almost everywhere, and are producing immense video stream
everyday.Unfortunately, only a small portion of these data are interesting to the users.Let’s take
traffics surveillance as example: only the part of videos that actually has moving human or
vehicles are useful, therefore, recording everything all day long would inevitably become a
waste of storage, and would make going through video a miserable work for searching and
tracking.
• To address this problem, object detection system could be of great importance as it can detect
the objects with a high level of accuracy.
• In fact, the need of object detection algorithm to be applied on resource constrained
device is highly in demand and not limited to monitor camera (e.g. phones, sports watch).
4. • Several similar object detecting systems have been developed but their accuracies
were very poor.One such system is Object detection using DPM(Deformable part
model).
• Moreover their processing speed is also extremely less(around 0.07fps).
5. • The process of object detection is done by analyzing the input image and determining
the number of locations, sizes, positions of the objects.
• Object detection is the base for object tracking and object recognition, whose results
directly affect the process and accuracy of object recognition.
• A common object detection method is color-based approach which detects objects
based on their color values.
6. INPUT
• Capturing image from the computer webcam in real time environment.
OUTPUT
• Recognize the object and display its name along with probability.
9. Environment Configuration
Before getting started with the coding part, we have to configure some
software to work properly with each other.
Required Softwares-
1.TensorFlow(python lib)/OpenCV
2.MinGW
3.Cmake
4.Sublime text editor
10. • TensorFlow is an open source software library for numerical
computation using data flow graphs. Nodes in the graph represent
mathematical operations, while the graph edges represent the
multidimensional data arrays (tensors) communicated between them.
11. • OpenCV is the most popular library for computer vision. Originally written in C/C++, it
now provides bindings for Python.
• OpenCV uses machine learning algorithms to search for faces within a picture. For
something as complicated as a face, there isn’t one simple test that will tell you if it
found a face or not. Instead, there are thousands of small patterns/features that must
be matched. The algorithms break the task of identifying the face into thousands of
smaller, bite-sized tasks, each of which is easy to solve. These tasks are also called
classifiers.
Refrence of papers:-
http://www.allresearchjournal.com/archives/2015/vol1issue9/PartG/1-9-20.pdf
12. The Whole process done by in two phases:
1. Training Phase
2. Object Identification Phase
14. • The training phase makes use of a hybrid model, integrating the synergy of two superior
classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM), which
have proven results in recognizing different types of patterns.
• In this model, CNN works as a trainable feature extractor and SVM performs as a recognizer.
This hybrid model automatically extracts features from the raw images and generates the
predictions.
• Experiments have been conducted on the well-known MNIST digit database have shown that
compared to other methods the above fusion method has achieved better results. A recognition
rate of 99.81% without rejection, and a recognition rate of 94.40% with 5.60% rejection coul
• According to COCO data set(released by Microsoft)The whole 300k images is classified into 90
class
• In this phase we are going to train our system by using the 300k images.
• We are going to store all the feature and images into the object database
16. • In OIP an image can be detected on the basis of its hue, saturation and color value.
• Extract the feature from image by using BCS (Boundary Contour System)and FCS(Feature
Contour System).
• Match the extracted features with defined class by using a Hybrid model of CNN and SVM.
• Based on the extracted features, display the result by competitive learning(means the closest
match).
17. • In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-
forward artificial neural networks that has successfully been applied to analyzing visual
imagery.
• CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing.They
are also known as shift invariant or space invariant artificial neural networks (SIANN), based on
their shared-weights architecture and translation invariance characteristics.
• CNNs use relatively little pre-processing compared to other image classification algorithms.
This means that the network learns the filters that in traditional algorithms were hand-
engineered. This independence from prior knowledge and human effort in feature design is a
major advantage.
29. A Support Vector Ma
●
chine (SVM) performs classification by finding the hyperplane that maximizes the margin between
the two classes
●
●
Notice: linearly separable and binary
It draws the widest channel, or street, between the two classes
The two class labels are +1 (positive examples) and -1 (negative examples)
35. • Microsoft classify all types of objects in 90 classes, containing 30k images. This Data
Set is known as COCO Data Set.
• CNN extract the feature from these data set and SVM classify the input according to
class.
All The data set resources link are give below:
• COCO DATA SET:
http://academictorrents.com/details/f993c01f3c268b5d57219a38f8ec73ee7524421a
36. CONT..
• Numerous face image sets are available on the web FERET face data set:
http://www.itl.nist.gov/iad/humanid/feret/
• UMIST data set:
http://images.ee.umist.ac.uk/danny/database.html
• Yale data set:
http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html
• AR data set:
http://cobweb.ecn.purdue.edu/%7Ealeix/aleix_face_DB.html
• CMU PIE data set:
http://www.ri.cmu.edu/projects/project_418.html
37. • Face recognition may also be implemented and we can try to generate a security alert
system that will give alerts when an intruder is going to trespass any unauthenticated
area based on his/her face recognition. But all authenticated people face imprints data
has to be provided to the system in advance.
• These systems can also be used to detect suspicious objects such as weapons in
places like country borders.
• Monitoring cameras are used almost everywhere, and are producing immense video
stream everyday.Instead of that we can use this system.
39. • The concept is well extendable in applications like Intelligent Robots,Automatic Guided
Vehicles.
• Enhancement of Security Systems to detect the suspicious behaviour along with detection of
weapons.
• Identify the suspicious movements of enemies on boarders with the help of night vision
cameras and many such applications.
• In the proposed method, background subtraction technique has been used that is simple and
fast. This technique is applicable where there is no movement of camera. For robotic
application or automated vehicle assistance system, due to the movement of camera,
backgrounds are continuously changing leading to implementation of some different
segmentation techniques.
40. 1.Viraktamath SV, Mukund Katti, Aditya Khatawkar,Pavan Kulkarni, “Face Detection and
Tracking using OpenCV,” The SIJ Transaction on Computer Networks & Communication
Engineering (CNCE), 2013, 1(3).
2.Pant A, Arora A, Kumar S, Arora RP. “Sophisticated Image Encryption Using OpenCV,”
International Journal of Advances Research in Computer Science and Software Engineering
3. Kevin Hughes – One more robot learn to see (http://kevinhughes.ca)
4.Belongie S, Malik J, Puzicha J. “Shape Matching and Object Recognition using shape contexts,”
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002
5.Tobias OJ, Seara R. “Image Segmentation by Histogram Thresholding Using Fuzzy Sets,” IEEE
Transactions on Image Processing, 2002; 11(12):1457-1465.
6. http://www.opencv.org