Multiple Vehicle Detection and Tracking in Hard Real Time

Abstract

A vision system has been developed that recognizes and tracks multiple vehicles from sequences of gray-scale images taken from a moving car in hard real-time. Recognition is accomplished by combining the analysis of single image frames with the analysis of the motion information provided by multiple consecutive image frames. In single image frames, cars are recognized by matching deformable gray-scale templates, by detecting image features, such as corners, and by evaluating how these features relate to each other. Cars are also recognized by differencing consecutive image frames and by tracking motion parameters that are typical for cars. The vision system utilizes the hard real-time operating system Maruti which guarantees that the timing constraints on the various processes of the vision system are satisfied. The dynamic creation and termination of tracking processes optimizes the amount of computational resources spent and allows fast detection and tracking of multiple cars. Experimental results demonstrate robust, real-time recognition and tracking over thousands of image frames. (Also cross-referenced as UMIACS-TR-96-52)