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The Scientific World Journal
Volume 2013, Article ID 405645, 11 pages
Research Article

Multi-Scale Locality-Constrained Spatiotemporal Coding for Local Feature Based Human Action Recognition

College of Information System and Manage, National University of Defense Technology, 109 Deya Road, Changsha, Hunan 410073, China

Received 2 July 2013; Accepted 21 August 2013

Academic Editors: R. Haber, P. Melin, and Y. Zhu

Copyright © 2013 Bin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a Multiscale Locality-Constrained Spatiotemporal Coding (MLSC) method to improve the traditional bag of features (BoF) algorithm which ignores the spatiotemporal relationship of local features for human action recognition in video. To model this spatiotemporal relationship, MLSC involves the spatiotemporal position of local feature into feature coding processing. It projects local features into a sub space-time-volume (sub-STV) and encodes them with a locality-constrained linear coding. A group of sub-STV features obtained from one video with MLSC and max-pooling are used to classify this video. In classification stage, the Locality-Constrained Group Sparse Representation (LGSR) is adopted to utilize the intrinsic group information of these sub-STV features. The experimental results on KTH, Weizmann, and UCF sports datasets show that our method achieves better performance than the competing local spatiotemporal feature-based human action recognition methods.