The document proposes a vision-based intelligent transportation system to characterize vehicle flow using video surveillance. It detects, counts, classifies, and estimates the speeds of vehicles passing through regions of interest in video frames in real-time. Experiments on four test videos showed the system achieved over 97% accuracy for vehicle counting and over 92% for vehicle classification. The system is robust to challenges like illumination changes, noise, and vehicle occlusion. Future work could include testing on longer videos with more challenging scenarios and acquiring ground truth data to further evaluate the speed estimation.
Poster Presentation of the 3rd IEEE Int. Conf. on ICIEV’14Habibur Rahman
The vehicular safety message feature is applied to avoid accident or collision avoidance on each vehicle. Analyzed the impact of IDM-IM and IDM-LC on AODV, AOMDV, DSDV and OLSR routing protocols in an urban scenario of Dhaka city. Recommend several concerns (drop rate, delay, jitter, route cost) before developing a realistic vehicular safety applications.
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Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
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NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Poster Presentation of the 3rd IEEE Int. Conf. on ICIEV’14Habibur Rahman
The vehicular safety message feature is applied to avoid accident or collision avoidance on each vehicle. Analyzed the impact of IDM-IM and IDM-LC on AODV, AOMDV, DSDV and OLSR routing protocols in an urban scenario of Dhaka city. Recommend several concerns (drop rate, delay, jitter, route cost) before developing a realistic vehicular safety applications.
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
James Goel, MIPI Technical Steering Group chair, shares a state-of-the-art MASS (MIPI Automotive SerDes Solutions) display architecture that leverages the latest MIPI DSI-2℠ protocols using VDC-M visually lossless compression algorithms to optimize pixel bandwidth within tightly constrained display systems.
Real-time Bangla License Plate Recognition System for Low Resource Video-base...MD Abdullah Al Nasim
Automatic License Plate Recognition systems aim to provide a solution for detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames. However, deploying such systems in the real world requires real-time performance in low-resource environments. In our paper, we propose a two-stage detection pipeline paired with Vision API that provides real-time inference speed along with consistently accurate detection and recognition performance. We used a haar-cascade classifier as a filter on top of our backbone MobileNet SSDv2 detection model. This reduces inference time by only focusing on high confidence detections and using them for recognition. We also impose a temporal frame separation strategy to distinguish between multiple vehicle license plates in the same clip. Furthermore, there are no publicly available Bangla license plate datasets, for which we created an image dataset and a video dataset containing license plates in the wild. We trained our models on the image dataset and achieved an AP (0.5) score of 86% and tested our pipeline on the video dataset and observed reasonable detection and recognition performance (82.7% detection rate, and 60.8% OCR F1 score) with real-time processing speed (27.2 frames per second).
How China NCAP is promoting the progress of automotive technical development ...Global NCAP
How China NCAP is promoting the progress of automotive technical development in China. Presentation given at the 2014 Global NCAP Annual Meeting. CATARC, Tianjin, China. 30 October 2014
Why all software teams move towards zero innovation speed - And what to do ab...Dirk Jan Swagerman
Software is eating the world and has become a major driver for economic growth. Companies are spending increased percentages of their R&D budget on software. Unfortunately, not all of this increased spending is of true economic benefit for the company and its customers. Legacy software costs eat significant portions of the budget. Industry is in need of breakthrough innovations to reduce software maintenance cost. Philips Healthtech Image Guided Therapy Systems successfully applied model-driven engineering techniques to reduce the software stack with over 1 million lines of code, significantly lowering the cost of maintenance.
In Critical Embedded Systems Electronics is central. This presentation focuses on how Automotive Electronics is developped to reach the stringent objectives of this critical systems domain.
Autoliv’s 3rd Generation Automotive Night Vision Camera with FLIR’s ISC0901 M...Yole Developpement
Infrared camera and electronic control unit for advanced driver assistance systems based on FLIR night vision imaging technology
Based on a high definition ISC0901 microbolometer from FLIR, the Autoliv night vision infrared camera targets the high-end automotive market using two FLIR cores. Based on the solid first design from 2009, the microbolometer offers better performance in definition and frame rate. The camera also embraces the quality approach with a complex optical system and powers its night vision using sophisticated numerical processing based on an Altera field-programmable gate array (FPGA) and custom algorithm.
The Autoliv night vision camera consists of two modules, the camera and a remote processing unit. The system is made very compact and easy to integrate for car makers.
The ISC0901 thermal camera uses FLIR’s 17µm pixel design, optimized for automotive applications. Based on vanadium oxide technology, the ISC0901 microbolometer features a 336x256 resolution wafer level package, achieving an incredibly compact design.
The ISC0901 is the automotive version of a surveillance microbolometer. By using wafer level package technology to encapsulate the microbolometer FLIR offers the best price performance ratio.
This report is divided into two parts, one focused on the microbolometer and the second on the other systems. It is based on a complete teardown analysis of the night vision camera and the associated electronic control unit. Using this, it provides the bill-of-material (BOM) and manufacturing cost of the infrared camera. The report also offers a complete physical analysis and manufacturing cost estimate of the infrared module, including the lens module and the microbolometer itself.
The report’s final component is a comparison between the characteristics of the FLIR ISC0901, FLIR Lepton 3 and the PICO384P from ULIS. The comparison highlights differences in technical choices made by the companies.
More information on that report at http://www.i-micronews.com/reports.html
The Performance Traction Control is an algorithm developed by Addfor to maximize the vehicle performance in every driving condition giving the vehicle the maximum available acceleration in exiting turns.
For any product details or customer specific questions our highly specialized team of Data Scientists and Engineers are available to answer you questions.
For more information visit: www.add-for.com
[Ubicomp'15]SakuraSensor: Quasi-Realtime Cherry-Lined Roads Detection throug...Ubi NAIST
SakuraSensor, a system which senses and shares the information of roads with flowering cherries by leveraging car-mounted smart-phones.
Honorable Mention Award of UbiComp2015.
James Goel, MIPI Technical Steering Group chair, shares a state-of-the-art MASS (MIPI Automotive SerDes Solutions) display architecture that leverages the latest MIPI DSI-2℠ protocols using VDC-M visually lossless compression algorithms to optimize pixel bandwidth within tightly constrained display systems.
Real-time Bangla License Plate Recognition System for Low Resource Video-base...MD Abdullah Al Nasim
Automatic License Plate Recognition systems aim to provide a solution for detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames. However, deploying such systems in the real world requires real-time performance in low-resource environments. In our paper, we propose a two-stage detection pipeline paired with Vision API that provides real-time inference speed along with consistently accurate detection and recognition performance. We used a haar-cascade classifier as a filter on top of our backbone MobileNet SSDv2 detection model. This reduces inference time by only focusing on high confidence detections and using them for recognition. We also impose a temporal frame separation strategy to distinguish between multiple vehicle license plates in the same clip. Furthermore, there are no publicly available Bangla license plate datasets, for which we created an image dataset and a video dataset containing license plates in the wild. We trained our models on the image dataset and achieved an AP (0.5) score of 86% and tested our pipeline on the video dataset and observed reasonable detection and recognition performance (82.7% detection rate, and 60.8% OCR F1 score) with real-time processing speed (27.2 frames per second).
How China NCAP is promoting the progress of automotive technical development ...Global NCAP
How China NCAP is promoting the progress of automotive technical development in China. Presentation given at the 2014 Global NCAP Annual Meeting. CATARC, Tianjin, China. 30 October 2014
Why all software teams move towards zero innovation speed - And what to do ab...Dirk Jan Swagerman
Software is eating the world and has become a major driver for economic growth. Companies are spending increased percentages of their R&D budget on software. Unfortunately, not all of this increased spending is of true economic benefit for the company and its customers. Legacy software costs eat significant portions of the budget. Industry is in need of breakthrough innovations to reduce software maintenance cost. Philips Healthtech Image Guided Therapy Systems successfully applied model-driven engineering techniques to reduce the software stack with over 1 million lines of code, significantly lowering the cost of maintenance.
In Critical Embedded Systems Electronics is central. This presentation focuses on how Automotive Electronics is developped to reach the stringent objectives of this critical systems domain.
Autoliv’s 3rd Generation Automotive Night Vision Camera with FLIR’s ISC0901 M...Yole Developpement
Infrared camera and electronic control unit for advanced driver assistance systems based on FLIR night vision imaging technology
Based on a high definition ISC0901 microbolometer from FLIR, the Autoliv night vision infrared camera targets the high-end automotive market using two FLIR cores. Based on the solid first design from 2009, the microbolometer offers better performance in definition and frame rate. The camera also embraces the quality approach with a complex optical system and powers its night vision using sophisticated numerical processing based on an Altera field-programmable gate array (FPGA) and custom algorithm.
The Autoliv night vision camera consists of two modules, the camera and a remote processing unit. The system is made very compact and easy to integrate for car makers.
The ISC0901 thermal camera uses FLIR’s 17µm pixel design, optimized for automotive applications. Based on vanadium oxide technology, the ISC0901 microbolometer features a 336x256 resolution wafer level package, achieving an incredibly compact design.
The ISC0901 is the automotive version of a surveillance microbolometer. By using wafer level package technology to encapsulate the microbolometer FLIR offers the best price performance ratio.
This report is divided into two parts, one focused on the microbolometer and the second on the other systems. It is based on a complete teardown analysis of the night vision camera and the associated electronic control unit. Using this, it provides the bill-of-material (BOM) and manufacturing cost of the infrared camera. The report also offers a complete physical analysis and manufacturing cost estimate of the infrared module, including the lens module and the microbolometer itself.
The report’s final component is a comparison between the characteristics of the FLIR ISC0901, FLIR Lepton 3 and the PICO384P from ULIS. The comparison highlights differences in technical choices made by the companies.
More information on that report at http://www.i-micronews.com/reports.html
The Performance Traction Control is an algorithm developed by Addfor to maximize the vehicle performance in every driving condition giving the vehicle the maximum available acceleration in exiting turns.
For any product details or customer specific questions our highly specialized team of Data Scientists and Engineers are available to answer you questions.
For more information visit: www.add-for.com
[Ubicomp'15]SakuraSensor: Quasi-Realtime Cherry-Lined Roads Detection throug...Ubi NAIST
SakuraSensor, a system which senses and shares the information of roads with flowering cherries by leveraging car-mounted smart-phones.
Honorable Mention Award of UbiComp2015.
2. Characterization of vehicle flow for intelligent
transportation systems
People’s Democratic Republic of Algeria
Ministry of Higher Education and Scientific Research
M’Hamed BOUGARA University – Boumerdes
Institute of Electrical and Electronic Engineering
Department of Electronics
MASTER
In Electrical and Electronic Engineering
Option: Telecommunications
Title
Presented by:
•Abdenour BOUAICHA
Supervisor:
Dr. Fatma KEROUH
6. The major categories of ITS?
Intelligent
Transportation
Systems
Advanced Traffic
Management
Systems
Advanced
Travelers
Information
Systems
Commercial
Vehicle
Operation
Advanced Public
Transportation
Systems
Advanced
Vehicle Control
Systems
Advanced Rural
Transportation
Systems
6
Intelligent Transportation
systems
8. Video quality Software
Blur effect Complexity
Bad illumination Execution time
Illumination changing …..
Shadow
Noise
Multiple vehicle occlusion
…..
8
Problems of visual based systems
9. Problems of visual based systems
ITS needs Information
Information requires Data.
Data relies on smart video surveillance.
Fast Robust Reliable
9
10. Intelligent Transportation
Systems
Our purpose?
Why?
• Accumulating the statistics.
Identifying critical flow time periods.
Determining the influence of large vehicles or pedestrians
on vehicular traffic flow.
Knowing the reasons of traffic congestion, road
degradation, and air pollution.
…… 10
Surveillance
camera
Image processing
techniques
Vehicle
detection,
counting,
classification and
speed estimation
11. Proposed vision based ITS
11
-Vehicle detection
and counting.
-Vehicle
Classification.
- Vehicle speed
estimation.
Processing
Preprocessing
Video
15. Proposed vision based ITS
15
Start
The camera is
activated
Preprocessing
The vehicle passing the
ROI
Vehicle detection, counting, classification,
and speed estimation
i++ Yes
No
Yes
End
No
16. Proposed ITS (Detection, counting, and
classification)
16
Proposed idea: Vehicle detected
Vehicle counted
and classified
17. Proposed ITS (Detection and
counting)
17
Start
Read ref
Preprocessing
Camera is
activated
Read rf
Preprocessing
Compute D
D >Th1
&&
D<=Th2
Count++End
Yes
No
Yes
No
19. Proposed ITS (Classification)
19
Start
Read ref
Preprocessing
Camera is
activated
End
CCF++
Vehicle is
detected
Vehicle
is
counted
CCF >=
Th3
Big
Small
No
No
No
No
Yes
Yes
Yes
Yes
21. Proposed ITS (Speed estimation)
21
Start
Camera is
activated
End
Vehicle is
counted in
first ROI
Vehicle is
counted in
second
ROI
COUNT2
COUNT1
COUNT1 !
= COUNT2
Spd++
Calculate the
Speed
Yes Yes
Yes
Yes
No
No
No
No
26. Experiments, Results and
DiscussionsVehicle detection and counting
ROI Video 1 Video 2 Video 3 Video 4
GT OR GT OR GT OR GT OR
ROI 1 13 13 13 13 5 6 4 4
ROI 2 6 6 9 9 9 9 17 14
ROI 3 12 12 8 8 15 14 20 15
ROI 4 13 13 13 13 16 16 22 26
Total 44 44 43 43 45 45 63 59
Accuracy 100% 100% 95.56% 93.65%
Runtime of
one
frames(sec)
0.0702 0.0467 0.1747 0.1713
26
27. Experiments, Results and
Discussions
Vehicle detection and classification
ROI Video 1 Video 2 Video 3 Video 4
S B RS RB S B RS RB S B RS RB S B RS RB
ROI 1 13 0 13 0 8 1 9 0 5 0 5 1 4 0 4 0
ROI 2 5 1 5 1 9 0 9 0 7 2 7 2 15 2 10 4
ROI 3 12 0 12 0 7 1 8 0 15 0 14 0 19 1 12 3
ROI 4 13 0 13 0 10 3 13 0 12 4 7 9 22 0 23 3
Accuracy
rate
100% 88.37% 88.88% 88.88%
Runtime
of one
frame
(sec)
0.0710 0.0463 0.1712 0.1737
2 27
30. Video The task The
running
-time
Percentage
accuracy
Count Class Speed Coun
t
Class
Video 1 Y Y - 0.0782 100% 100%
Video 2 Y Y Y 0.0938 100% 90.91
%
Video 3 Y Y Y 0.1901 95.56
%
88.88
%
Video 4 Y Y Y 0.2031 93.85
%
88.88
%
Total - - - - 97.3% 92.42
%
30
Experiments, Results and Discussions
32. Analyzing the proposed approach
The strengths
Real time
Simple
Robust against to bad and changing illumination
Robust against to the noise
Robust against to the blur effect
Robust against to the occlusion
Simple camera
32
33. The weaknesses
Vehicle passing through portion of ROI.
Fixed camera.
Vehicle congestion.
33
Analyzing the proposed approach
34. Conclusion and perspectives
Introducing the concepts of ITS.
Showing the problems on visual based systems.
Developing a simple and real-time ITS that detects, counts, classifies
and estimates the speed of vehicles.
Proposed ITS relies on selected best ROI which in middle and inside
of the lane with neither small length nor big.
Separating the ROI solved problem of multiple vehicles occlusion.
Using LBP have shown that our system is robust against bad and
changing illumination.
Using adaptive thresholds solve the problem of noise and blur effect.
The percentage accuracy of counting is: 97.3%.
The percentage accuracy of classification is: 92.42%
34
35. Conclusion and perspectives
Perspectives:
-Test the proposed system on long videos that
contains more challenging problems.
-Consider more classes in the classification step
(small, medium, and big vehicles).
-Acquire videos with ground truth to test deeply the
speed estimation part.
-Plan to build android application.
35