TY - GEN
T1 - MPEG CDVS Feature Trajectories for Action Recognition in Videos
AU - Dasari, Radhakrishna
AU - Chen, Chang Wen
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - Visual Action Recognition on mobile phones is a challenging problem. Mobile and wearable devices deal with power, memory, computational and hardware constraints, which mandate robust and lightweight algorithmic implementations for sophisticated vision applications, like action recognition. Compact Descriptors for Visual Search (CDVS) is an MPEG7 standard for an accelerated visual search on mobiles. In our work, we propose a mobile action recognition framework which classifies actions by tracking CDVS feature trajectories of human subjects. The proposed method capitalizes on the sparse, salient and memory efficient properties of CDVS features. Although our recognition accuracies on standard action datasets KTH, UCF50, and HMDB is not superior to the CNN based methods, our work explores and proves the feasibility of using CDVS features for action recognition.
AB - Visual Action Recognition on mobile phones is a challenging problem. Mobile and wearable devices deal with power, memory, computational and hardware constraints, which mandate robust and lightweight algorithmic implementations for sophisticated vision applications, like action recognition. Compact Descriptors for Visual Search (CDVS) is an MPEG7 standard for an accelerated visual search on mobiles. In our work, we propose a mobile action recognition framework which classifies actions by tracking CDVS feature trajectories of human subjects. The proposed method capitalizes on the sparse, salient and memory efficient properties of CDVS features. Although our recognition accuracies on standard action datasets KTH, UCF50, and HMDB is not superior to the CNN based methods, our work explores and proves the feasibility of using CDVS features for action recognition.
KW - Action Recognition
KW - Computer Vision
KW - Visual Search
UR - https://www.scopus.com/pages/publications/85050114599
U2 - 10.1109/MIPR.2018.00069
DO - 10.1109/MIPR.2018.00069
M3 - Conference contribution
T3 - Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
SP - 301
EP - 304
BT - Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018
Y2 - 10 April 2018 through 12 April 2018
ER -