Qualitative and quantitative car tracking from a range image sequence

In this paper, we present a car tracking system which provides quantitative and qualitative notion estimates of the tracked car simultaneously from a moving observer. First, we construct three motion models (constant velocity, constant acceleration, and turning) to describe the qualitative motion of a moving car. Then the models are incorporated into the Extended Kalman Filters to perform quantitative tracking. Finally, we develop an Extended Interacting Multiple Model (EIMM) algorithm to manage the switching between models and to output both qualitative and quantitative motion estimates of the tracked car. Accurate motion modeling and efficient model management result in a high performance tracking system. The experimental results on simulated and real data demonstrate that our tracking system Is reliable and robust, and, runs in real-time. The multiple motion representations make the system useful in various autonomous driving tasks.