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Abstract

In this paper, we present a car tracking system which provides quantitative and qualitative motion 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 algorithm (EIMM) 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.

Keywords

car tracking, multiple motion models, IMM, qualitative motion estimate

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): NavLab

Number of pages: 6

Text Reference

Liang Zhao and Chuck Thorpe, "Qualitative and Quantitative Car Tracking from a Range Image Sequence," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 1998, pp. 496-501.

BibTeX Reference

@inproceedings{Zhao_1998_544,
   author = "Liang Zhao and Chuck Thorpe",
   title = "Qualitative and Quantitative Car Tracking from a Range Image Sequence",
   booktitle = "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
   pages = "496-501",
   publisher = "IEEE",
   month = "June",
   year = "1998",
}

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