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Conference papers

Action Recognition based on a mixture of RGB and Depth based skeleton

Abstract : In this paper, we study how different skeleton extraction methods affect the performance of action recognition. As shown in previous work skeleton information can be exploited for action recognition. Nevertheless, skeleton detection problem is already hard and very often it is difficult to obtain reliable skeleton information from videos. In this paper , we compare two skeleton detection methods: the depth-map based method used with Kinect camera and RGB based method that uses Deep Convolutional Neural Networks. In order to balance the pros and cons of mentioned skeleton detection methods w.r.t. action recognition task, we propose a fusion of classifiers trained based on each skeleton detection method. Such fusion lead to performance improvement. We validate our approach on CAD-60 and MSRDailyActiv-ity3D, achieving state-of-the-art results.
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Contributor : Srijan Das Connect in order to contact the contributor
Submitted on : Tuesday, November 21, 2017 - 1:09:46 PM
Last modification on : Thursday, January 20, 2022 - 5:29:34 PM


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Srijan Das, Michal Koperski, François Bremond, Gianpiero Francesca. Action Recognition based on a mixture of RGB and Depth based skeleton. AVSS 2017 - 14-th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Aug 2017, Lecce, Italy. ⟨hal-01639504⟩



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