Human Action Recognition Based on Large Margin Nearest Neighbor

In order to recognize actions in daily life for improving the service quality of the home service robot and providing a safe and comfortable environment for human, a metric learning method based on Manhattan distance is proposed for human action recognition. Firstly, Kinect is used to acquire the joint point data of human action. Then, the action sensitive feature set is constructed based on the joint point data, that is, the structure vectors of human and their corresponding angles are constructed based on the human joint point data, and the length of each sample is normalized. The large margin nearest neighbor (LMNN) method is adopted to obtain the transformation matrix L by learning Manhattan distance. And the normalized data is mapped to a better features space. Finally, the k-nearest neighbor algorithm is utilized to recognize the human actions. Based on our dataset, the accuracy of 97% is achieved. Experimental results show that the LMNN algorithm can improve the distribution of the data (that is, the intra class distance is reduced, and the inter class distance is expanded), and can complete the human action recognition task.