Feature Tracking and Motion Compensation for Action Recognition
Hirofumi Uemura, Shota Ishikawa, Krystian Mikolajczyk · 2008
This paper discusses an approach to human action recognition via local fea-ture tracking and robust estimation of background motion. The main contri-bution is a robust feature extraction algorithm based on KLT tracker and SIFT as well as a method for estimating dominant planes in the scene. Multiple in-terest point detectors are used to provide large number of features for every frame. The motion vectors for the features are estimated using optical flow and SIFT based matching. The features are combined with image segmenta-tion to estimate dominant homographies, and then separated into static and moving ones regardless the camera motion. The action recognition approach can handle camera motion, zoom, human appearance variations, background clutter and occlusion. The motion compensation shows very good accuracy on a number of test sequences. The recognition system is extensively com-pared to state-of-the art action recognition methods and the results are im-proved. 1