Abstract
In this paper, we propose a new motion descriptor which uses only block matching vectors. This is a different and simple approach considering that most works on the field are based on the gradient of image intensities. The block matching method returns displacements vectors as a motion information. Our method computes this information to obtain orientation tensors and to generate the final descriptor. It is considered a self-descriptor, since it depends only on the input video. The global tensor descriptor is evaluated by a classification of KTH, UCF11 and Hollywood2 video datasets with a non-linear SVM classifier. Our results indicate that the method runs fast and has fairly competitive results compared to similar approaches. It is suitable when the time response is a major application issue. It also generates compact descriptors which are desirable to describe large datasets.
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Figueiredo, A.M.O., Maia, H.A., Oliveira, F.L.M., Mota, V.F., Vieira, M.B. (2014). A Video Tensor Self-descriptor Based on Block Matching. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham. https://doi.org/10.1007/978-3-319-09153-2_30
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DOI: https://doi.org/10.1007/978-3-319-09153-2_30
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