Fast algorithms for object orientation determination

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Abstract
This paper concerns the determination of orientation of road vehicles from monocular intensity images. A novel algorithm is presented which exploits known physical and geometric knowledge about traffic scenes to allow fast and model-independent determination of vehicle orientations. The algorithm eliminates the need for symbolic image feature extraction and image-to-model matching, and the computational cost is substantially reduced. In fact, since the algorithm only requires local gradient data, object orientation can be determined from the input video data on-the-fly, and the overall algorithm can easily be implemented in real-time. The algorithm is tested with both indoor and outdoor data. Successful results are obtained for a variety of vehicles in routine traffic scenes.

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