TY - GEN
T1 - Mining discriminative 3D Poselet for cross-view action recognition
AU - Wang, Jiang
AU - Nie, Xiaohan
AU - Xia, Yin
AU - Wu, Ying
PY - 2014
Y1 - 2014
N2 - This paper presents a novel approach to cross-view action recognition. Traditional cross-view action recognition methods typically rely on local appearance/motion features. In this paper, we take advantage of the recent developments of depth cameras to build a more discriminative cross-view action representation. In this representation, an action is characterized by the spatio-temporal configuration of 3D Poselets, which are discriminatively discovered with a novel Poselet mining algorithm and can be detected with view-invariant 3D Poselet detectors. The Kinect skeleton is employed to facilitate the 3D Poselet mining and 3D Poselet detectors learning, but the recognition is solely based on 2D video input. Extensive experiments have demonstrated that this new action representation significantly improves the accuracy and robustness for cross-view action recognition.
AB - This paper presents a novel approach to cross-view action recognition. Traditional cross-view action recognition methods typically rely on local appearance/motion features. In this paper, we take advantage of the recent developments of depth cameras to build a more discriminative cross-view action representation. In this representation, an action is characterized by the spatio-temporal configuration of 3D Poselets, which are discriminatively discovered with a novel Poselet mining algorithm and can be detected with view-invariant 3D Poselet detectors. The Kinect skeleton is employed to facilitate the 3D Poselet mining and 3D Poselet detectors learning, but the recognition is solely based on 2D video input. Extensive experiments have demonstrated that this new action representation significantly improves the accuracy and robustness for cross-view action recognition.
UR - http://www.scopus.com/inward/record.url?scp=84904627571&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904627571&partnerID=8YFLogxK
U2 - 10.1109/WACV.2014.6836043
DO - 10.1109/WACV.2014.6836043
M3 - Conference contribution
AN - SCOPUS:84904627571
SN - 9781479949854
T3 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
SP - 634
EP - 639
BT - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
PB - IEEE Computer Society
T2 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Y2 - 24 March 2014 through 26 March 2014
ER -