Reverse Testing Image Set Model Based Multi-view Human Action Recognition

Abstract

Recognizing human activities from videos becomes a hot research topic in computer vision, but many studies show that action recognition based on single view cannot obtain satisfying performance, thus, many researchers put their attentions on multi-view action recognition, but how to mine the relationships among different views still is a challenge problem. Since video face recognition algorithm based on image set has proved that image set algorithm can effectively mine the complementary properties of different views image, and achieves satisfying performance. Thus, Inspired by these, image set is utilized to mine the relationships among multi-view action recognition. However, the studies show that the sample number of gallery and query set in video face recognition based on image set will affect the algorithm performance, and several ten to several hundred samples is supplied, but, in multi-view action recognition, we only have 3---5 views samples in each query set, which will limit the effect of image set. In order to solve the issues, reverse testing image set model called RTISM based multi-view human action recognition is proposed. We firstly extract dense trajectory feature for each camera, and then construct the shared codebook by k-means for all cameras, after that, Bag-of-Word BoW weight scheme is employed to code these features for each camera; Secondly, for each query set, we will compute the compound distance with each image subset in gallery set, after that, the scheme of the nearest image subset called RTIS is chosen to add into the query set; Finally, RTISM is optimized where the query set and RTIS are whole reconstructed by the gallery set, thus, the relationship of different actions among gallery set and the complementary property of different samples among query set are meanwhile excavated. Large scale experimental results on two public multi-view action3D datasets - Northwestern UCLA and CVS-MV-RGBD-Single, show that the reconstruction of query set over gallery set is very effectively, and RTIS added into query set is very helpful for classification, what is more, the performance of RTISM is comparable to the state-of-the-art methods.