Improving traffic law enforcement accuracy with violating vehicle tracking and | |
| Author | Phani, Kommanaboina Bala Krishna |
| Subject(s) | Automobiles-Tracking Video surveillance Law enforcement Traffic violations-Prevention |
| Note | A research study submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science |
| Publisher | Asian Institute of Technology |
| Abstract | Traffic surveillance is one of the important measures that can reduce number of road acci dents and collisions. Cameras can play an essential role in collision detection, traffic man agement, and traffic law enforcement. Applications using computer vision include vehicle tracking and speed estimation. Lane change violations are one type of moving violation in which a vehicle crosses a solid white line demarking the lane. In collecting evidence from a lane change violation, we may simply extract the frame in which the violation occurred but it would be better to extract the "best frame" in the video sequence. This method results in reducing the redundant and incorrect data frames. The dataset used in this study contains overview traffic footage of Phuket,Thailand. The system is trained for close traffic area with semi-densed traffic for Object detection and tracking. The main aim of this research study is the selection of "best frame"(containing license plate of the vehicle) for a violating track among the sequence of frames it has been detected in the video. The "best frame" is extracted on the basis of different criteria such as area, magnitude, sharpness of detected frames. The "best frame" is extracted by taking the maximum of product of area and sharpness, where the magnitude is measured on the basis of Frobenius norm and sharpness value is measured on the basis of Laplace operator. A list of comparison with violating frame and "best frame" are shown in the results. With the help of Vinfo, a real time surveillance system for vehicle license plates’ Optical Character Recognition the analysis of my hypothesis is measured, which shows "best frame" for every image except for fragmented tracks. |
| Year | 2020 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Dailey, Matthew N.; |
| Examination Committee(s) | Mongkol Ekpanyapong;Attaphongse Taparugssanagorn; |
| Scholarship Donor(s) | AIT Fellowship; |
| Degree | Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 2020 |