The Approach for Action Recognition Based on the Reconstructed Phase Spaces

This paper presents a novel method of human action recognition, which is based on the reconstructed phase space. Firstly, the human body is divided into 15 key points, whose trajectory represents the human body behavior, and the modified particle filter is used to track these key points for self-occlusion. Secondly, we reconstruct the phase spaces for extracting more useful information from human action trajectories. Finally, we apply the semisupervised probability model and Bayes classified method for classification. Experiments are performed on the Weizmann, KTH, UCF sports, and our action dataset to test and evaluate the proposed method. The compare experiment results showed that the proposed method can achieve was more effective than compare methods

Human action recognition based on boosted feature selection and naive Bayes nearest-neighbor classification

In this paper we propose a method of feature selection using the AdaBoost algorithm for action recognition. Instead of detecting spatio-temporal interest points and using a ‘bag of features’ approach, we use densely sampled descriptors, either 3D-SIFT or 3D-HOG, and select the most discriminative subset using the AdaBoost algorithm. We obtain maximal accuracy with just 200 of the 3217 possible raw 3D features from each video sequence. Using the extremely simple naive Bayes nearest-neighbor (NBNN) classifier with the most discriminative 3D-SIFT features, we obtain accuracies of: 92.7%, 99.4%, 92.3% and 38.1% on the KTH, Weizmann, IXMAS and HMDB51 datasets, respectively. We also observe that the errors are reasonably equitably distributed across the different action classes