Sparse representation of point trajectories for action classification

Ravishankar Sivalingam, Guruprasad Somasundaram, Vineet Bhatawadekar, Vassilios Morellas, Nikolaos P Papanikolopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Action classification is an important component of human-computer interaction. Trajectory classification is an effective way of performing action recognition with significant success reported in the literature. We compare two different representation schemes, raw multivariate time-series data and the covariance descriptors of the trajectories, and apply sparse representation techniques for classifying the various actions. The features are sparse coded using the Orthogonal Matching Pursuit algorithm, and the gestures and actions are classified based on the reconstruction residuals. We demonstrate the performance of our approach on standardized datasets such as the Australian Sign Language (AusLan) and UCF Motion Capture datasets, collected using high-quality motion capture systems, as well as motion capture data obtained from a Microsoft Kinect sensor.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Robotics and Automation, ICRA 2012
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3601-3606
Number of pages6
ISBN (Print)9781467314039
DOIs
StatePublished - Jan 1 2012
Event 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 - Saint Paul, MN, United States
Duration: May 14 2012May 18 2012

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
CountryUnited States
CitySaint Paul, MN
Period5/14/125/18/12

Fingerprint

Trajectories
Human computer interaction
Time series
Data acquisition
Sensors

Cite this

Sivalingam, R., Somasundaram, G., Bhatawadekar, V., Morellas, V., & Papanikolopoulos, N. P. (2012). Sparse representation of point trajectories for action classification. In 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 (pp. 3601-3606). [6224777] (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2012.6224777

Sparse representation of point trajectories for action classification. / Sivalingam, Ravishankar; Somasundaram, Guruprasad; Bhatawadekar, Vineet; Morellas, Vassilios; Papanikolopoulos, Nikolaos P.

2012 IEEE International Conference on Robotics and Automation, ICRA 2012. Institute of Electrical and Electronics Engineers Inc., 2012. p. 3601-3606 6224777 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sivalingam, R, Somasundaram, G, Bhatawadekar, V, Morellas, V & Papanikolopoulos, NP 2012, Sparse representation of point trajectories for action classification. in 2012 IEEE International Conference on Robotics and Automation, ICRA 2012., 6224777, Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., pp. 3601-3606, 2012 IEEE International Conference on Robotics and Automation, ICRA 2012, Saint Paul, MN, United States, 5/14/12. https://doi.org/10.1109/ICRA.2012.6224777
Sivalingam R, Somasundaram G, Bhatawadekar V, Morellas V, Papanikolopoulos NP. Sparse representation of point trajectories for action classification. In 2012 IEEE International Conference on Robotics and Automation, ICRA 2012. Institute of Electrical and Electronics Engineers Inc. 2012. p. 3601-3606. 6224777. (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ICRA.2012.6224777
Sivalingam, Ravishankar ; Somasundaram, Guruprasad ; Bhatawadekar, Vineet ; Morellas, Vassilios ; Papanikolopoulos, Nikolaos P. / Sparse representation of point trajectories for action classification. 2012 IEEE International Conference on Robotics and Automation, ICRA 2012. Institute of Electrical and Electronics Engineers Inc., 2012. pp. 3601-3606 (Proceedings - IEEE International Conference on Robotics and Automation).
@inproceedings{58958d0a2f9d4c4087986baca6bd1bfc,
title = "Sparse representation of point trajectories for action classification",
abstract = "Action classification is an important component of human-computer interaction. Trajectory classification is an effective way of performing action recognition with significant success reported in the literature. We compare two different representation schemes, raw multivariate time-series data and the covariance descriptors of the trajectories, and apply sparse representation techniques for classifying the various actions. The features are sparse coded using the Orthogonal Matching Pursuit algorithm, and the gestures and actions are classified based on the reconstruction residuals. We demonstrate the performance of our approach on standardized datasets such as the Australian Sign Language (AusLan) and UCF Motion Capture datasets, collected using high-quality motion capture systems, as well as motion capture data obtained from a Microsoft Kinect sensor.",
author = "Ravishankar Sivalingam and Guruprasad Somasundaram and Vineet Bhatawadekar and Vassilios Morellas and Papanikolopoulos, {Nikolaos P}",
year = "2012",
month = "1",
day = "1",
doi = "10.1109/ICRA.2012.6224777",
language = "English (US)",
isbn = "9781467314039",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3601--3606",
booktitle = "2012 IEEE International Conference on Robotics and Automation, ICRA 2012",

}

TY - GEN

T1 - Sparse representation of point trajectories for action classification

AU - Sivalingam, Ravishankar

AU - Somasundaram, Guruprasad

AU - Bhatawadekar, Vineet

AU - Morellas, Vassilios

AU - Papanikolopoulos, Nikolaos P

PY - 2012/1/1

Y1 - 2012/1/1

N2 - Action classification is an important component of human-computer interaction. Trajectory classification is an effective way of performing action recognition with significant success reported in the literature. We compare two different representation schemes, raw multivariate time-series data and the covariance descriptors of the trajectories, and apply sparse representation techniques for classifying the various actions. The features are sparse coded using the Orthogonal Matching Pursuit algorithm, and the gestures and actions are classified based on the reconstruction residuals. We demonstrate the performance of our approach on standardized datasets such as the Australian Sign Language (AusLan) and UCF Motion Capture datasets, collected using high-quality motion capture systems, as well as motion capture data obtained from a Microsoft Kinect sensor.

AB - Action classification is an important component of human-computer interaction. Trajectory classification is an effective way of performing action recognition with significant success reported in the literature. We compare two different representation schemes, raw multivariate time-series data and the covariance descriptors of the trajectories, and apply sparse representation techniques for classifying the various actions. The features are sparse coded using the Orthogonal Matching Pursuit algorithm, and the gestures and actions are classified based on the reconstruction residuals. We demonstrate the performance of our approach on standardized datasets such as the Australian Sign Language (AusLan) and UCF Motion Capture datasets, collected using high-quality motion capture systems, as well as motion capture data obtained from a Microsoft Kinect sensor.

UR - http://www.scopus.com/inward/record.url?scp=84864493220&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864493220&partnerID=8YFLogxK

U2 - 10.1109/ICRA.2012.6224777

DO - 10.1109/ICRA.2012.6224777

M3 - Conference contribution

SN - 9781467314039

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 3601

EP - 3606

BT - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012

PB - Institute of Electrical and Electronics Engineers Inc.

ER -