Action recognition based on divide-and-conquer

Abstract

Recently, deep convolutional neural networks have made great breakthroughs in the field of action recognition. Since sequential video frames have a lot of redundant information, compared with dense sampling, sparse sampling network can also achieve good results. Due to sparse sampling’s limitation of access to information, this paper mainly discusses how to further improve the learning ability of the model based on sparse sampling. We proposed a model based on divide-and-conquer, which use a threshold α to determine whether action data require sparse sampling or dense local sampling for learning. Finally, our approach obtains the state-the-of-art performance on the datasets of HMDB51 (72.4%) and UCF101 (95.3%).