Human action recognition with line and flow histograms

We present a compact representation for human action recognition in videos using line and optical flow histograms. We introduce a new shape descriptor based on the distribution of lines which are fitted to boundaries of human figures. By using an entropy-based approach, we apply feature selection to densify our feature representation, thus, minimizing classification time without degrading accuracy. We also use a compact representation of optical flow for motion information. Using line and flow histograms together with global velocity information, we show that high-accuracy action recognition is possible, even in challenging recording conditions. 1

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Nazli Ikizler, Pinar Duygulu, Ramazan Gokberk Cinbis
• Keywords: Compact Representation, Computer Vision, Human Action Recognition, ICPR 2008, Optical Flow Histograms