Combination of edge element and optical flow estimates for 3D-model-based vehicle tracking in traffic image sequences

A model-based vehicle tracking system for the evaluation of inner-city traffic video sequences has been systematically tested on about 15 minutes of real world video data. Methodological improvements during preparatory test phases affected-among other changes-the combination of edge element and optical flow estimates in the measurement process and a more consequent exploitation of background knowledge. The explication of this knowledge in the form of models facilitates the evaluation of video data for different scenes by exchanging the scene-dependent models. An extensive series of experiments with a large test sample demonstrates that the current version of our system appears to have reached a relative optimum: further interactive tuning of tracking parameters does no longer promise to improve the overall system performance significantly. Even the incorporation of further knowledge regarding vehicle and scene geometry or illumination has to cope with an increasing level of interaction between different knowledge sources and system parameters. Our results indicate that model-based tracking of rigid objects in monocular image sequences may have to be reappraised more thoroughly than anticipated during the recent past.