Abstract
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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Romero, A., Gouiffés, M., Lacassagne, L. (2013). Covariance Descriptor Multiple Object Tracking and Re-identification with Colorspace Evaluation. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37484-5_33
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DOI: https://doi.org/10.1007/978-3-642-37484-5_33
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