Real-time video of traffic scenes contain a wealth of information not available from conventional point detectors. In addition to the instantaneous, wide-area coverage provided by image data, image sequences capture the dynamic aspects of the traffic. Initially, researchers concentrated on minimizing hardware complexity, and thus cost, at the expense of sophisticated algorithms that could more fully exploit the information inherent in image data. If image data could be processed in real-time to produce a track file for each object of interest, then the traffic flow through the scene would be fully characterized for traffic management purposes. This paper presents the status of work in process at the Environmental Research Institute of Michigan (ERIM) to develop real-time image processing algorithms for detecting and tracking vehicles in actual traffic settings. The image processing techniques for detecting and tracking will be illustrated, the corresponding computational resources will be described, and preliminary results on typical video sequences will be presented.