Action Recognition Using Random Forest Prediction with Combined Pose-based and Motion-based Features
In this paper we propose a novel human action recognition system that uses random forest prediction with statistically combined pose-based and motion-based features. Given a set of training and test image sequences (videos) we first adopt recent techniques that extract low-level features: motion and pose features. Motion-based features which represent motion patterns in the consecutive images are formed by 3D Haar-like features. Pose-based features are obtained by the calculation of scale invariant contour-based features. Then using statistical methods we combine these low-level features to a novel compact representation which describes the global motion and the global pose information in the whole image sequence. Finally Random Forest classification is employed to recognize actions in the test sequences by using this novel representation. Our experimental results on KTH and Weizmann datasets have shown that the combination of pose-based and motion-based features increased the system recognition accuracy. The proposed system also achieved classification rates comparable to the state-of-the-art approaches.