Abstract
Sparse representation is one of the most popular methods for human activity recognition. Sparse representation describes a video by a set of independent descriptors. Each of these descriptors usually captures the local information of the video. These features are then mapped to another space, using Fisher Vectors, and an SVM is used for clustering them. One of the sparse representation methods proposed in the literature uses trajectories as features. Trajectories have been shown to be discriminative in many previous works on human activity recognition. In this paper, a more formal definition is given to trajectories and a new more effective trajectory shape descriptor is proposed. We tested the proposed method against our challenging dataset and demonstrated through experiments that our new trajectory descriptor outperforms the previously existing main shape descriptor with a good margin. For example, in one case the obtained results had a 5.58% improvement, compared to the existing trajectory shape descriptor. We run our tests over sparse feature sets, and we are able to reach comparable results to a dense sampling method, with fewer computations.
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Habashi, P., Boufama, B., Ahmad, I.S. (2017). A Better Trajectory Shape Descriptor for Human Activity Recognition. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_37
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DOI: https://doi.org/10.1007/978-3-319-59876-5_37
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