基于骨骼特征Hough变换的行为识别研究

为有效地表征人体行为的时空特征,将骨骼特征通过Hough变换后建立人体的动作表示.具体来说,采用OpenPose获取视频帧人体骨骼关键点,之后构建骨骼关节并映射到Hough空间,将骨骼关节轨迹转换为点迹,然后角度和轨迹特征的FV(Fisher vector)编码融合作为线性SVM分类器的输入.在经典公共数据集KTH、Weizmann、KARD和Drone-Action上,实验结果表明Hough变换提升了特征的鲁棒性,提高了人体行为识别的性能.

Action recognition based on Hough transform of skeletal features

In order to effectively describe the spatio-temporal information of human action, this paper proposed skeletal features by Hough transform to construct the motion representation. Specifically, under the OpenPose framework, the method obtained the key points of human skeletons in video frames, and built the skeleton joints, then converted the trajectories of skeleton joints into the point trajectories in Hough space. After that, FV(Fisher vector) based approach coded the angle and trajectory features, and then the concatenate method combined the coded features to input the linear SVM classifier. On the classical public datasets KTH, Weizmann, KARD and Drone-Action. Experimental results show that the Hough transform promotes the robustness of features, and improves the performance of human action recognition.