基于区域特征融合网络的群组行为识别

doi:10.16451/j.cnki.issn1003-6059.201912007

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Abstract The existing group activity recognition methods cannot take full advantage of spatial information of the scene and the computational complexity of them is high. To solve these problems, a group activity recognition method based on regional feature fusion is proposed. Firstly, the convolution neural network is utilized to extract regional features of the scene, and then the regional features are split, arranged and combined into a series of regional feature sequences according to spatial position. Finally, long short term memory network is utilized to fuse regional feature sequences. Additionally, multilevel and multimodal strategies are adopted to improve the performance of the proposed method. Experiments on Collective and Volleyball datasets show that the proposed method achieves better performance.

Key words： Group Activity Recognition Spatial Information Regional Feature Convolutional Neural Network(CNN) Long Short Term Memory Network(LSTM)

Received: 26 July 2019

ZTFLH:

TP 391

Fund:Supported by Natural Science Foundation of Anhui Province(No.1808085MF168)

Corresponding Authors: YANG Xingming, Ph.D., associate professor. His research interests include computer control, Internet of things, image processing and machine learning.

About author:: FAN Loumiao, master student. His research interests include image processing and deep learning.

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YANG Xingming,FAN Loumiao. Group Activity Recognition Based on Regional Feature Fusion Network[J]. , 2019, 32(12): 1116-1121.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201912007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I12/1116

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