TY - GEN
T1 - Human action recognition using distribution of oriented rectangular patches
AU - Ikizler, Nazli
AU - Duygulu, Pinar
PY - 2007
Y1 - 2007
N2 - We describe a "bag-of-rectangles" method for representing and recognizing human actions in videos. In this method, each human pose in an action sequence is represented by oriented rectangular patches extracted over the whole body. Then, spatial oriented histograms are formed to represent the distribution of these rectangular patches. In order to carry the information from the spatial domain described by the bag-of-rectangles descriptor to temporal domain for recognition of the actions, four different methods are proposed. These are namely, (i) frame by frame voting, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis by rectangular patches, (iii) a classifier based approach using SVMs, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the descriptor. The detailed experiments are carried out on the action dataset of Blank et. al. High success rates (100%) prove that with a very simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations.
AB - We describe a "bag-of-rectangles" method for representing and recognizing human actions in videos. In this method, each human pose in an action sequence is represented by oriented rectangular patches extracted over the whole body. Then, spatial oriented histograms are formed to represent the distribution of these rectangular patches. In order to carry the information from the spatial domain described by the bag-of-rectangles descriptor to temporal domain for recognition of the actions, four different methods are proposed. These are namely, (i) frame by frame voting, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis by rectangular patches, (iii) a classifier based approach using SVMs, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the descriptor. The detailed experiments are carried out on the action dataset of Blank et. al. High success rates (100%) prove that with a very simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations.
UR - https://www.scopus.com/pages/publications/38149111348
U2 - 10.1007/978-3-540-75703-0_19
DO - 10.1007/978-3-540-75703-0_19
M3 - Conference contribution
AN - SCOPUS:38149111348
SN - 9783540757023
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 271
EP - 284
BT - Human Motion - Understanding, Modeling, Capture and Animation - Second Workshop, Human Motion 2007, Proceedings
PB - Springer Verlag
T2 - 2nd Workshop on Human Motion Understanding, Modeling, Capture and Animation
Y2 - 20 October 2007 through 20 October 2007
ER -