A feature-based vehicle flow analysis and measurement system is proposed for a realtime traffic surveillance system. The system includes moving object segmentation, background updating, feature extraction, and vehicle tracking and classification. Moving object segmentation is firstused to extract the contour of vehicles. By analyzing the contours of vehicles and their corresponding minimal bounding box, salient discriminative features of vehicles are obtained. The tracking of moving targets is then achieved by comparing extracted features and by measuring the minimum distance between two consecutive images. To increase the accuracy of vehicle classification, the temporal correlation of moving objects tracked between video frames is taken into consideration. In addition, the velocity of each vehicle and the vehicle flow through the field of vision are calculated by analyzing the features of vehicles. Experimental results show that classification rates of 96.4% and 92.7% for cars and bikes, respectively, can be achieved using the feature of aspect ratio. The bikes here refer to motorcycles, scooters, or bicycles. The average accuracy of vehicle flow measurement of 96.9% is obtained, indicating the feasibility of the proposed method.
|Number of pages||15|
|Journal||Journal of Information Hiding and Multimedia Signal Processing|
|Publication status||Published - 2012 Dec 21|
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition