Smart City Gnosys
Smart city article details
| Title | Traffic Surveillance And Speed Limit Violation Detection System |
|---|---|
| ID_Doc | 58679 |
| Authors | Kumar T.; Kushwaha D.S. |
| Year | 2017 |
| Published | Journal of Intelligent and Fuzzy Systems, 32, 5 |
| DOI | http://dx.doi.org/10.3233/JIFS-169308 |
| Abstract | India has shown rapid economic development and this has forced the country to adopt smart traffic management and surveillance process. The intelligent traffic management and surveillance is the basic need for development of smart cities in India. The Intelligent traffic surveillance includes the detection of moving vehicles, estimation of their speed and extracting its registration number. This paper proposes an efficient and novel approach for the detection of moving vehicles as well as estimation of their speeds for detection of any speed limit violation by using a single camera in daylight or properly illuminated environment. The proposed approach detects and tracks the vehicle passing through the surveillance area and maintains the record of vehicles position. The Region of Interest (ROI) of the approach is the dark shaded area formed under the vehicles road clearance area instead of the vehicles. The average detection accuracy achieved by proposed approach is 93.32. This is on an average 7.54 higher than other existing approaches. The proposed approach uses cropping operation to minimize the scope of any false positive detection on both sides of road. The average tracking accuracy of the proposed approach is about 92.2. The tracking accuracy achieved by the proposed approach is on an average 2.2 higher than the other leading approaches. The average speed estimation error in proposed approach is 2.2 Space;km/h which is lower than other existing leading approaches. The proposed approach is also time and space efficient over the existing approaches. © 2017 - IOS Press and the authors. All rights reserved. |
| Author Keywords | Background subtraction; camera calibration; contours processing; image filtering; moving object detection; thresholding |
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