基于伪3D残差网络与交互关系建模的群组行为识别方法  

Group Activity Recognition Method Based on Pseudo 3D Residual Network and Interaction Modeling

作　　者：丰艳[1] 张甜甜 王传旭[1] FENG Yan;ZHANG Tian-tian;WANG Chuan-xu(School of Information Science & Technology,Qingdao University of Science and Technology,Qingdao,Shandong 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院,山东青岛266061

出　　处：《电子学报》2020年第7期1269-1275,共7页Acta Electronica Sinica

基　　金：国家自然科学基金面上项目(No.61672305);国家自然科学基金青年科学基金(No.61702295)。

摘　　要：针对复杂场景下群组行为特征的多样性以及交互关系难以建模的问题,提出一种全新的分层网络架构.第一层网络,利用伪3D残差网络与图卷积网络相结合捕获交互关系特征;第二层网络,利用伪3D残差网络捕获群组全局场景时空特征.根据上述特征之间的互补作用对它们的群组行为决策输出,提出一种权重自适应调整决策融合算法,对上面两层网络的群组行为类别自适应计算重要性权重,实现决策融合.该方法在CAD和CAE上分别取得了91.4%和97.9%的平均识别精度.For the diversity of group behavior characteristics in complex scenes and the problem of difficult interaction modeling,this paper proposes a new two-layered network architecture.The first layer of network combines a pseudo 3D residual network with a graph convolution network to capture the interaction characteristics.The second layer of network,uses the pseudo 3D residual network to capture the group global scene spatio-temporal characteristics.Based on the complementary role of the above features,their group behavior decisions are fused with a weight adaptive adjustment algorithm,which adaptively calculates importance weights for the group behavior categories predicted by the above two channels,and realizes decision fusion of the different prediction results.The method has achieved 91.4%and 97.9%average recognition accuracy on CAD and CAE respectively.

关 键 词：群组行为识别 交互关系建模 自适应决策融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]