Covariance Descriptor Multiple Object Tracking and Re-identification with Colorspace Evaluation

This paper addresses the multi-target tracking problem with the help of a matching method where moving objects are detected in each frame, tracked when it is possible and matched by similarity of covariance matrices when difficulties arrive. Three contributions are proposed. First, a compact vector based on color invariants and Local Binary Patterns Variance is compared to more classical features vectors. To accelerate object re-identification, our second proposal is the use of a more efficient arrangement of the covariance matrices. Finally, a multiple-target algorithm with special attention in occlusion handling, merging and separation of the targets is analyzed. Our experiments show the relevance of the method, illustrating the trade-off that has to be made between distinctiveness, invariance and compactness of the features.

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