文章基本信息

标题：Action Recognition Using Action Sequences Optimization and Two-Stream 3D Dilated Neural Network
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作者：Xin Xiong ; Weidong Min ; Qing Han 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2022
卷号：2022
DOI：10.1155/2022/6608448
语种：English
出版社：Hindawi Publishing Corporation
摘要：Effective extraction and representation of action information are critical in action recognition. The majority of existing methods fail to recognize actions accurately because of interference of background changes when the proportion of high-activity action areas is not reinforced and by using RGB flow alone or combined with optical flow. A novel recognition method using action sequences optimization and two-stream fusion network with different modalities is proposed to solve these problems. The method is based on shot segmentation and dynamic weighted sampling, and it reconstructs the video by reinforcing the proportion of high-activity action areas, eliminating redundant intervals, and extracting long-range temporal information. A two-stream 3D dilated neural network that integrates features of RGB and human skeleton information is also proposed. The human skeleton information strengthens the deep representation of humans for robust processing, alleviating the interference of background changes, and the dilated CNN enlarges the receptive field of feature extraction. Compared with existing approaches, the proposed method achieves superior or comparable classification accuracies on benchmark datasets UCF101 and HMDB51.