Download e-book for kindle: Human Action Recognition with Depth Cameras by Jiang Wang, Zicheng Liu, Ying Wu (auth.)

By Jiang Wang, Zicheng Liu, Ying Wu (auth.)

ISBN-10: 3319045601

ISBN-13: 9783319045603

ISBN-10: 331904561X

ISBN-13: 9783319045610

Action acceptance expertise has many real-world functions in human-computer interplay, surveillance, video retrieval, retirement domestic tracking, and robotics. The commoditization of intensity sensors has additionally spread out additional purposes that weren't possible sooner than. this article makes a speciality of function illustration and laptop studying algorithms for motion acceptance from intensity sensors. After providing a accomplished evaluate of the state-of-the-art, the authors then offer in-depth descriptions in their lately constructed characteristic representations and computing device studying recommendations, together with lower-level intensity and skeleton gains, higher-level representations to version the temporal constitution and human-object interactions, and have choice thoughts for occlusion dealing with. This paintings permits the reader to quick familiarize themselves with the most recent study, and to realize a deeper knowing of lately built innovations. will probably be of serious use for either researchers and practitioners.

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14] also applies spatio-temporal occupancy patterns, but all the cells in the grid have the same size, and the number of cells is empirically set. Yang and Tian [15] proposes a dimension-reduced skeleton feature, and [16] developed a histogram of gradient feature over depth motion maps. Instead of carefully developing good features, this chapter tries to learn semi-local features automatically from the data, and we show that this learning-based approach achieves good results. 3 Random Occupancy Patterns The proposed method treats a depth sequence as a 4D volume, and defines the value of a pixel in this volume I (x, y, z, t) to be either 1 or 0, depending on whether there is a point in the 4D volume at this location.

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Human action recognition using star skeleton. In: Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks, pp. 171–178, New York, USA (2006) 14. : Learning recurrent neural networks with hessian-free optimization. In: ICML (2011) 15. : Motion templates for automatic classification and retrieval of motion capture data. In: Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation, pp. 137–146, Eurographics Association (2006) 16. A. Time series clustering: complex is simpler!

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Human Action Recognition with Depth Cameras by Jiang Wang, Zicheng Liu, Ying Wu (auth.)


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