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

• Published in: TheScientificWorld Vol. 2013; no. 2013; pp. 1 - 11
• Main Authors: Zhang, Maojun, Xu, Wei, Wang, Wei, Liu, Yu, Wang, Bin
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Classification; Codes; Coding; Dictionaries; Feature recognition; Human activity recognition; Human motion; Humans; Image processing; Information processing; Methods; Models, Theoretical; Motor Activity; Multiscale analysis; Pattern recognition; Pattern Recognition, Automated - methods; Signal processing; Software; Spacetime; Support Vector Machine; Video data
• ISSN: 2356-6140; 1537-744X; 1537-744X
• DOI: 10.1155/2013/405645

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.