Human Action Recognition Combined With Object Detection

Most of the research methods in the field of human action recognition extract relevant features from the original video frames. These methods introduce more or less redundant background information, which brings more noise to the neural network. In order to solve the problem of background information interference, large amount of redundant information in video frames, unbalanced sample classification and difficult classification of individual classes, this paper proposes a new algorithm for human action recognition combined with object detection. Firstly, the object detection mechanism is added in the process of human action recognition, so that the neural network has a focus on learning the motion information of the human body. Secondly, the video is segmentally and randomly sampled to establish long-term time domain modeling across the entire video segment. Finally, action recognition is performed through an improved neural network loss function. In this work, a large number of experimental analyses are performed on the popular human action recognition datasets UCF101 and HDBM51. The accuracy of human action recognition (RGB images only) is 96.0% and 75.3%, respectively, which is significantly higher than the state-of-the-art human action recognition algorithms. Copyright © 2020 Acta Automatica Sinica. All rights reserved.