| 摘要(英) |
With the advancement of computer technologies and the urgent demand for social security, researches on vision-based surveillance grow more and more important. The developed systems can be employed in various applications, such as security service and traffic monitoring. The advantages of using such systems include the saving of human resources, the reducing of costs, and the providing of consistent performance.
The correct detection of traffic violation events plays a very important role in traffic surveillance. In this thesis, a novel approach is presented to detect and track large vehicles driving on specific lanes (especially the inner lane). In the proposed approach, activity map is firstly generated to detect lanes, and useful data is extracted including the lane width and the vanishing point to facilitate the later task. Secondly, vehicle detector is devised to find large vehicles in the detection area by utilizing the techniques of temporal difference and Sobel edge detection. In the tracking process, Kalman filter is adopted to accomplish the task. Here, a time-varying state transition matrix is devised to adapt the velocity variations in 2-D images. Moreover, dual mode tracker is developed for more effective tracking.
Experiments were conducted on a variety of real world traffic scenes. The average accuracy rates of large vehicle detection and tracking are 91.3% and 84.4%, respectively. Experimental results reveal that the proposed approach is feasible and effective for large vehicle detection and tracking. |
| 參考文獻 |
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