摘要: |
This paper describes a real-time system for tracking pedestrians in sequences of grayscale images acquired by a stationary camera. This system is suitable for use in applications which aim to increase the efficiency and safety of existing traffic systems. One such application is pedestrian control at intersections. The output of the system is the spatio-temporal coordinates of each pedestrian during the period the pedestrian is visible. The system is model-based in the sense that it uses a simple rectangular model which has its own location and velocity for pedestrians. The authors' system uses three levels of abstraction. The lowest level, which is at the image level, deals with raw images. At this level, background subtraction is performed and the result is passed to the second level. This level, which they call the blobs level, deals with blobs obtained by segmenting the subtracted image. It performs robust blob tracking with no regard to what or who the blobs represent. The output of this level is the tracked blobs which are in turn passed to the final level, the pedestrians level. The pedestrians level deals with pedestrian models and depends on the tracked blobs as the only source of input. By doing this, they avoid trying to infer information about pedestrians directly from raw images, a process that is highly sensitive to noise. The pedestrians level makes use of Kalman filtering to predict and estimate pedestrian attributes. The filtered attributes consitute the output of this level which is the output of the system. Their system was designed to be robust to high levels of noise and particularly to deal with difficult situations such as partial or full occlusions of pedestrians. The system was implemented on a Datacube MaxVideo 20 equipped with a Datacube Max860 and was able to achieve a peak performance of over 20 frames per second. Experimental results based on indoor and outdoor scenes have shown that the system is robust under many difficult traffic situations. |