A fast binary pair-based video descriptor for action recognition

Roberto Leyva; Victor Sanchez; Chang-Tsun Li

doi:10.1109/ICIP.2016.7533148

A fast binary pair-based video descriptor for action recognition

Roberto Leyva, Victor Sanchez, Chang-Tsun Li

Computing and Mathematics

Research output: Book chapter/Published conference paper › Conference paper › peer-review

6 Citations (Scopus)

4 Downloads (Pure)

Abstract

Inspired by the binary-based descriptors (e.g. LBP, ALOHA, FREAK, BRISK), we propose the 3D Binary Pair Differences (3DBPD) video descriptor for action recognition. By comparing several spatio-temporal sub-regions around interests points, our descriptor is a feature vector with a dimensionality of up to 30% smaller than that of existing state-of-the-art descriptors. We demonstrate the effectiveness of the 3DBPD descriptor for action recognition with a SVM classifier and a simple Bag Of Video Words (BOV) generated using k-means. The proposed descriptor has very competitive recognition rates compared to other state-of-the-art descriptors, with an outstanding performance in terms of speed. Additionally, the 3DBPD descriptor requires a small codebook compared to those required by existing BOV-based descriptors.

Original language	English
Title of host publication	2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	4185-4189
Number of pages	5
ISBN (Print)	9781467399616
DOIs	https://doi.org/10.1109/ICIP.2016.7533148
Publication status	Published - 2016
Event	2016 23rd IEEE International Conference on Image Processing: ICIP 2016 - Phoenix Convention Center, Phoenix, United States Duration: 25 Sep 2016 → 28 Sep 2016 Conference number: 125190 http://2016.ieeeicip.org/default.asp

Conference

Conference	2016 23rd IEEE International Conference on Image Processing
Country	United States
City	Phoenix
Period	25/09/16 → 28/09/16
Internet address	http://2016.ieeeicip.org/default.asp

Access to Document

10.1109/ICIP.2016.7533148

Cite this

@inproceedings{21df9531817a4e53b2c2ce9cc5cedcec,

title = "A fast binary pair-based video descriptor for action recognition",

abstract = "Inspired by the binary-based descriptors (e.g. LBP, ALOHA, FREAK, BRISK), we propose the 3D Binary Pair Differences (3DBPD) video descriptor for action recognition. By comparing several spatio-temporal sub-regions around interests points, our descriptor is a feature vector with a dimensionality of up to 30% smaller than that of existing state-of-the-art descriptors. We demonstrate the effectiveness of the 3DBPD descriptor for action recognition with a SVM classifier and a simple Bag Of Video Words (BOV) generated using k-means. The proposed descriptor has very competitive recognition rates compared to other state-of-the-art descriptors, with an outstanding performance in terms of speed. Additionally, the 3DBPD descriptor requires a small codebook compared to those required by existing BOV-based descriptors.",

author = "Roberto Leyva and Victor Sanchez and Chang-Tsun Li",

year = "2016",

doi = "10.1109/ICIP.2016.7533148",

language = "English",

isbn = "9781467399616",

pages = "4185--4189",

booktitle = "2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2016 23rd IEEE International Conference on Image Processing : ICIP 2016 ; Conference date: 25-09-2016 Through 28-09-2016",

url = "http://2016.ieeeicip.org/default.asp",

}

Leyva, R, Sanchez, V & Li, C-T 2016, A fast binary pair-based video descriptor for action recognition. in 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings., 7533148, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 4185-4189, 2016 23rd IEEE International Conference on Image Processing, Phoenix, United States, 25/09/16. https://doi.org/10.1109/ICIP.2016.7533148

A fast binary pair-based video descriptor for action recognition. / Leyva, Roberto; Sanchez, Victor; Li, Chang-Tsun.

2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. United States : IEEE, Institute of Electrical and Electronics Engineers, 2016. p. 4185-4189 7533148.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - A fast binary pair-based video descriptor for action recognition

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AU - Sanchez, Victor

AU - Li, Chang-Tsun

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N2 - Inspired by the binary-based descriptors (e.g. LBP, ALOHA, FREAK, BRISK), we propose the 3D Binary Pair Differences (3DBPD) video descriptor for action recognition. By comparing several spatio-temporal sub-regions around interests points, our descriptor is a feature vector with a dimensionality of up to 30% smaller than that of existing state-of-the-art descriptors. We demonstrate the effectiveness of the 3DBPD descriptor for action recognition with a SVM classifier and a simple Bag Of Video Words (BOV) generated using k-means. The proposed descriptor has very competitive recognition rates compared to other state-of-the-art descriptors, with an outstanding performance in terms of speed. Additionally, the 3DBPD descriptor requires a small codebook compared to those required by existing BOV-based descriptors.

AB - Inspired by the binary-based descriptors (e.g. LBP, ALOHA, FREAK, BRISK), we propose the 3D Binary Pair Differences (3DBPD) video descriptor for action recognition. By comparing several spatio-temporal sub-regions around interests points, our descriptor is a feature vector with a dimensionality of up to 30% smaller than that of existing state-of-the-art descriptors. We demonstrate the effectiveness of the 3DBPD descriptor for action recognition with a SVM classifier and a simple Bag Of Video Words (BOV) generated using k-means. The proposed descriptor has very competitive recognition rates compared to other state-of-the-art descriptors, with an outstanding performance in terms of speed. Additionally, the 3DBPD descriptor requires a small codebook compared to those required by existing BOV-based descriptors.

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DO - 10.1109/ICIP.2016.7533148

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