Behavior recognition via sparse spatio-temporal features

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Standard

Behavior recognition via sparse spatio-temporal features. / Dollár, Piotr; Rabaud, Vincent; Cottrell, Garrison; Belongie, Serge.

I: Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS, 2005, s. 65-72.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Dollár, P, Rabaud, V, Cottrell, G & Belongie, S 2005, 'Behavior recognition via sparse spatio-temporal features', Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS, s. 65-72. https://doi.org/10.1109/VSPETS.2005.1570899

APA

Dollár, P., Rabaud, V., Cottrell, G., & Belongie, S. (2005). Behavior recognition via sparse spatio-temporal features. Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS, 65-72. https://doi.org/10.1109/VSPETS.2005.1570899

Vancouver

Dollár P, Rabaud V, Cottrell G, Belongie S. Behavior recognition via sparse spatio-temporal features. Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS. 2005;65-72. https://doi.org/10.1109/VSPETS.2005.1570899

Author

Dollár, Piotr ; Rabaud, Vincent ; Cottrell, Garrison ; Belongie, Serge. / Behavior recognition via sparse spatio-temporal features. I: Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS. 2005 ; s. 65-72.

Bibtex

@inproceedings{2e8f6f4d0e0344729977013c4c645924,
title = "Behavior recognition via sparse spatio-temporal features",
abstract = "A common trend in object recognition is to detect and lever-age the use of sparse, informative feature points, The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas to the spatio-temporal case. For this purpose, we show that the direct 3D counterparts to commonly used 2D interest point detectors are inadequate, and we propose an alternative. Anchoring off of these interest points, we devise a recognition algorithm based on spatio-temporally windowed data. We present recognition results on a variety of datasets including both human and rodent behavior.",
author = "Piotr Doll{\'a}r and Vincent Rabaud and Garrison Cottrell and Serge Belongie",
year = "2005",
doi = "10.1109/VSPETS.2005.1570899",
language = "English",
pages = "65--72",
journal = "Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS",
note = "2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS ; Conference date: 15-10-2005 Through 16-10-2005",

}

RIS

TY - GEN

T1 - Behavior recognition via sparse spatio-temporal features

AU - Dollár, Piotr

AU - Rabaud, Vincent

AU - Cottrell, Garrison

AU - Belongie, Serge

PY - 2005

Y1 - 2005

N2 - A common trend in object recognition is to detect and lever-age the use of sparse, informative feature points, The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas to the spatio-temporal case. For this purpose, we show that the direct 3D counterparts to commonly used 2D interest point detectors are inadequate, and we propose an alternative. Anchoring off of these interest points, we devise a recognition algorithm based on spatio-temporally windowed data. We present recognition results on a variety of datasets including both human and rodent behavior.

AB - A common trend in object recognition is to detect and lever-age the use of sparse, informative feature points, The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas to the spatio-temporal case. For this purpose, we show that the direct 3D counterparts to commonly used 2D interest point detectors are inadequate, and we propose an alternative. Anchoring off of these interest points, we devise a recognition algorithm based on spatio-temporally windowed data. We present recognition results on a variety of datasets including both human and rodent behavior.

UR - http://www.scopus.com/inward/record.url?scp=33846622081&partnerID=8YFLogxK

U2 - 10.1109/VSPETS.2005.1570899

DO - 10.1109/VSPETS.2005.1570899

M3 - Conference article

AN - SCOPUS:33846622081

SP - 65

EP - 72

JO - Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS

JF - Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS

T2 - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS

Y2 - 15 October 2005 through 16 October 2005

ER -

ID: 302054466