TY - GEN
T1 - Combining skeletal pose with local motion for human activity recognition
AU - Xu, Ran
AU - Agarwal, Priyanshu
AU - Kumar, Suren
AU - Krovi, Venkat N.
AU - Corso, Jason J.
PY - 2012
Y1 - 2012
N2 - Recent work in human activity recognition has focused on bottom-up approaches that rely on spatiotemporal features, both dense and sparse. In contrast, articulated motion, which naturally incorporates explicit human action information, has not been heavily studied; a fact likely due to the inherent challenge in modeling and inferring articulated human motion from video. However, recent developments in data-driven human pose estimation have made it plausible. In this paper, we extend these developments with a new middle-level representation called dynamic pose that couples the local motion information directly and independently with human skeletal pose, and present an appropriate distance function on the dynamic poses. We demonstrate the representative power of dynamic pose over raw skeletal pose in an activity recognition setting, using simple codebook matching and support vector machines as the classifier. Our results conclusively demonstrate that dynamic pose is a more powerful representation of human action than skeletal pose.
AB - Recent work in human activity recognition has focused on bottom-up approaches that rely on spatiotemporal features, both dense and sparse. In contrast, articulated motion, which naturally incorporates explicit human action information, has not been heavily studied; a fact likely due to the inherent challenge in modeling and inferring articulated human motion from video. However, recent developments in data-driven human pose estimation have made it plausible. In this paper, we extend these developments with a new middle-level representation called dynamic pose that couples the local motion information directly and independently with human skeletal pose, and present an appropriate distance function on the dynamic poses. We demonstrate the representative power of dynamic pose over raw skeletal pose in an activity recognition setting, using simple codebook matching and support vector machines as the classifier. Our results conclusively demonstrate that dynamic pose is a more powerful representation of human action than skeletal pose.
KW - Activity Recognition
KW - Dynamic Pose
KW - Human Pose
UR - http://www.scopus.com/inward/record.url?scp=84864687445&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864687445&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-31567-1_11
DO - 10.1007/978-3-642-31567-1_11
M3 - Conference contribution
AN - SCOPUS:84864687445
SN - 9783642315664
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 114
EP - 123
BT - Articulated Motion and Deformable Objects - 7th International Conference, AMDO 2012, Proceedings
T2 - 7th International Conference on Articulated Motion and Deformable Objects, AMDO 2012
Y2 - 11 July 2012 through 13 July 2012
ER -