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Conference Paper
Action Recognition with Depth Maps using HOG Descriptors of Multi-View Motion Appearance and History
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- Authors
- DoHyung Kim, Woo-han Yun, Ho-Sub Yoon, Jaehong Kim
- Issue Date
- 2014-08
- Citation
- International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM) 2014, pp.126-130
- Language
- English
- Type
- Conference Paper
- Abstract
- The goal of this work is to recognize human actions only using depth maps without additional joints information. As a practical solution, we present a novel volumetric representation of global shape of depth motion, Depth Motion Appearance (DMA). The proposed framework also extracts dynamic information of the body movements called Depth Motion History (DMH), an extended version of motion history image. In the framework, a huge amount of data of an action video is summarized into concise action representation maps observed from multi-view. A histogram of oriented gradients then describes local appearances and shapes of the DMAs and DMHs, which results in more compact and discriminative action representation. The presented method has been compared with the state-of-the-art approaches on a public dataset. The experimental result demonstrates that our approach achieves a better and more stable performance with a relatively smaller feature maps and lower complexity.
- KSP Keywords
- Action recognition, Action representation, Depth Map, Depth motion, Dynamic information, Experimental Result, Feature map, HOG descriptors, Human action, Motion history image, Multi-view