TY - GEN
T1 - A framework for hand gesture recognition and spotting using sub-gesture modeling
AU - Malgireddy, Manavender R.
AU - Corso, Jason J.
AU - Setlur, Srirangaraj
AU - Govindaraju, Venu
AU - Mandalapu, Dinesh
PY - 2010
Y1 - 2010
N2 - Hand gesture interpretation is an open research problem in Human Computer Interaction (HCI), which involves locating gesture boundaries (Gesture Spotting) in a continuous video sequence and recognizing the gesture. Existing techniques model each gesture as a temporal sequence of visual features extracted from individual frames which is not efficient due to the large variability of frames at different timestamps. In this paper, we propose a new sub-gesture modeling approach which represents each gesture as a sequence of fixed sub-gestures (a group of consecutive frames with locally coherent context) and provides a robust modeling of the visual features. We further extend this approach to the task of gesture spotting where the gesture boundaries are identified using a filler model and gesturecompletion model. Experimental results show that the proposed method outperforms state-of-the-art Hidden Conditional Random Fields (HCRF) based methods and baseline gesture spotting techniques.
AB - Hand gesture interpretation is an open research problem in Human Computer Interaction (HCI), which involves locating gesture boundaries (Gesture Spotting) in a continuous video sequence and recognizing the gesture. Existing techniques model each gesture as a temporal sequence of visual features extracted from individual frames which is not efficient due to the large variability of frames at different timestamps. In this paper, we propose a new sub-gesture modeling approach which represents each gesture as a sequence of fixed sub-gestures (a group of consecutive frames with locally coherent context) and provides a robust modeling of the visual features. We further extend this approach to the task of gesture spotting where the gesture boundaries are identified using a filler model and gesturecompletion model. Experimental results show that the proposed method outperforms state-of-the-art Hidden Conditional Random Fields (HCRF) based methods and baseline gesture spotting techniques.
UR - http://www.scopus.com/inward/record.url?scp=78149473003&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149473003&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.921
DO - 10.1109/ICPR.2010.921
M3 - Conference contribution
AN - SCOPUS:78149473003
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 3780
EP - 3783
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
ER -