A Novel Method for Tracking and Counting Pedestrians in Real-Time Using a Single Camera
This paper presents a real-time system for pedestrian tracking in sequences of grayscale images acquired by a stationary camera. The objective is to integrate this system with a traffic con- trol application such as a pedestrian control scheme at intersec- tions. The proposed approach can also be used to detect and track humans in front of vehicles. Furthermore, the proposed schemes can be employed for the detection of several diverse traffic ob- jects of interest (vehicles, bicycles, etc.) The system outputs the spatio-temporal coordinates of each pedestrian during the period the pedestrian is in the scene. Processing is done at three levels: raw images, blobs, and pedestrians. Blob tracking is modeled as a graph optimization problem. Pedestrians are modeled as rectan- gular patches with a certain dynamic behavior. Kalman filtering is used to estimate pedestrian parameters. The system was imple- mented on a Datacube MaxVideo 20 equipped with a Datacube Max860 and was able to achieve a peak performance of over 30 frames per second. Experimental results based on indoor and out- door scenes demonstrated the system's robustness under many dif- ficult situations such as partial or full occlusions of pedestrians.