Discriminative human action classification using locality-constrained linear coding
•We propose using the locality-constrained linear coding for action classification.•Our sequence descriptor includes cell, block, and subsequence descriptors.•We use maximum pooling and a logistic regression classifier to encode each sequence.•We demonstrate the effectiveness of our algorithm on bot...
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| Vydáno v: | Pattern recognition letters Ročník 72; s. 62 - 71 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.03.2016
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| ISSN: | 0167-8655, 1872-7344 |
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| Abstract | •We propose using the locality-constrained linear coding for action classification.•Our sequence descriptor includes cell, block, and subsequence descriptors.•We use maximum pooling and a logistic regression classifier to encode each sequence.•We demonstrate the effectiveness of our algorithm on both depth and RGB videos.
We propose a Locality-constrained Linear Coding (LLC) based algorithm that captures discriminative information of human actions in spatio-temporal subsequences of videos. The input video is divided into equally spaced overlapping spatio-temporal subsequences. Each subsequence is further divided into blocks and then cells. The spatio-temporal information in each cell is represented by a Histogram of Oriented 3D Gradients (HOG3D). LLC is then used to encode each block. We show that LLC gives more stable and repetitive codes compared to the standard Sparse Coding. The final representation of a video sequence is obtained using logistic regression with ℓ2 regularization and classification is performed by a linear SVM. The proposed algorithm is applicable to conventional and depth videos. Experimental comparison with ten state-of-the-art methods on three depth video and two conventional video databases shows that the proposed method consistently achieves the best performance. |
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| AbstractList | •We propose using the locality-constrained linear coding for action classification.•Our sequence descriptor includes cell, block, and subsequence descriptors.•We use maximum pooling and a logistic regression classifier to encode each sequence.•We demonstrate the effectiveness of our algorithm on both depth and RGB videos.
We propose a Locality-constrained Linear Coding (LLC) based algorithm that captures discriminative information of human actions in spatio-temporal subsequences of videos. The input video is divided into equally spaced overlapping spatio-temporal subsequences. Each subsequence is further divided into blocks and then cells. The spatio-temporal information in each cell is represented by a Histogram of Oriented 3D Gradients (HOG3D). LLC is then used to encode each block. We show that LLC gives more stable and repetitive codes compared to the standard Sparse Coding. The final representation of a video sequence is obtained using logistic regression with ℓ2 regularization and classification is performed by a linear SVM. The proposed algorithm is applicable to conventional and depth videos. Experimental comparison with ten state-of-the-art methods on three depth video and two conventional video databases shows that the proposed method consistently achieves the best performance. |
| Author | Mian, Ajmal Rahmani, Hossein Mahmood, Arif Huynh, Du Q. |
| Author_xml | – sequence: 1 givenname: Hossein surname: Rahmani fullname: Rahmani, Hossein organization: School of Computer Science and Software Engineering, The University of Western Australia, Crawley WA 6009, Australia – sequence: 2 givenname: Du Q. orcidid: 0000-0003-3080-9655 surname: Huynh fullname: Huynh, Du Q. email: du.huynh@uwa.edu.au organization: School of Computer Science and Software Engineering, The University of Western Australia, Crawley WA 6009, Australia – sequence: 3 givenname: Arif surname: Mahmood fullname: Mahmood, Arif organization: School of Computer Science and Software Engineering, The University of Western Australia, Crawley WA 6009, Australia – sequence: 4 givenname: Ajmal surname: Mian fullname: Mian, Ajmal organization: School of Computer Science and Software Engineering, The University of Western Australia, Crawley WA 6009, Australia |
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| Cites_doi | 10.1109/ICCV.2013.228 10.1109/34.910878 10.1109/TPAMI.2011.235 10.1007/s11263-013-0636-x 10.1016/j.cviu.2010.10.002 10.1007/s11263-005-1838-7 10.1145/1922649.1922653 |
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| Snippet | •We propose using the locality-constrained linear coding for action classification.•Our sequence descriptor includes cell, block, and subsequence... |
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| SubjectTerms | Human action classification Locality-constrained linear coding Sparse coding SVM classifier |
| Title | Discriminative human action classification using locality-constrained linear coding |
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