title: Multi-scale locality-constrained spatiotemporal coding for local feature based human action recognition.
abstract: 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.
authors.affiliation: College of Information System and Manage, National University of Defense Technology, 109 Deya Road, Changsha, Hunan 410073, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Wang
authors.forename: Yu
authors.initials: Y
authors.lastname: Liu
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Wei
authors.initials: W
authors.lastname: Xu
authors.forename: Maojun
authors.initials: M
authors.lastname: Zhang
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D009043:Motor Activity; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software; D060388:Support Vector Machine; D014741:Video Recording