Multi-Scale Locality-Constrained Spatiotemporal Coding for Local Feature Based Human Action Recognition
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...
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          | Published in | TheScientificWorld Vol. 2013; no. 2013; pp. 1 - 11 | 
|---|---|
| Main Authors | , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Cairo, Egypt
          Hindawi Publishing Corporation
    
        01.01.2013
     John Wiley & Sons, Inc Wiley  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 2356-6140 1537-744X 1537-744X  | 
| DOI | 10.1155/2013/405645 | 
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| 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. | 
    
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| AbstractList | 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. 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.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.  | 
    
| Audience | Academic | 
    
| Author | Liu, Yu Wang, Wei Zhang, Maojun Xu, Wei Wang, Bin  | 
    
| AuthorAffiliation | College of Information System and Manage, National University of Defense Technology, 109 Deya Road, Changsha, Hunan 410073, China | 
    
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24194681$$D View this record in MEDLINE/PubMed | 
    
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| CitedBy_id | crossref_primary_10_1155_2014_238234 crossref_primary_10_1155_2018_9032945 crossref_primary_10_1155_2014_723213  | 
    
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| ContentType | Journal Article | 
    
| Copyright | Copyright © 2013 Bin Wang et al. COPYRIGHT 2013 John Wiley & Sons, Inc. Copyright © 2013 Bin Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2013 Bin Wang et al. 2013  | 
    
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| Snippet | We propose a Multiscale Locality-Constrained Spatiotemporal Coding (MLSC) method to improve the traditional bag of features (BoF) algorithm which ignores the... We propose a Multiscale Locality‐Constrained Spatiotemporal Coding (MLSC) method to improve the traditional bag of features (BoF) algorithm which ignores the...  | 
    
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| SubjectTerms | 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  | 
    
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| Title | Multi-Scale Locality-Constrained Spatiotemporal Coding for Local Feature Based Human Action Recognition | 
    
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