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Proceedings Paper

Action description using point clouds
Author(s): Wenping Liu; Yongfeng Jiang; Haili Wang; Liang Zhang
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Paper Abstract

An action description method named as Motion History Point Cloud (MHPC) is proposed in this paper. MHPC compresses an action into a three-dimensional point cloud in which depth information is required. In MHPC, the spatial coordinate channels are used to record the motion foreground, and the color channels are used to record the temporal variation. Due to containing depth information, MHPC can depict an action more meticulous than Motion History Image (MHI). MHPC can serve as a pre-processed input for various classification methods, such as Bag of Words and Deep Learning. An action recognition scheme is provided as an application example of MHPC. In this scheme, Harris3D detector and Fast Point Feature Histogram (FPFH) are used to extract and describe features from MHPC. Then, Bag of Words and multiple classification Support Vector Machine (SVM) are used to do action recognition. The experiments show that rich features can be extracted from MHPC to support the subsequent action recognition even after downsampling. The feasibility and effectiveness of MHPC are also verified by comparing the above scheme with two similar methods.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104430X (19 June 2017); doi: 10.1117/12.2280342
Show Author Affiliations
Wenping Liu, Civil Aviation Univ. of China (China)
Yongfeng Jiang, Public Security Sub-bureau of Wenzhou Airport (China)
Haili Wang, Civil Aviation Univ. of China (China)
Liang Zhang, Civil Aviation Univ. of China (China)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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