针对石油化工场景下传统的人体行为识别算法只关注人员自身行为,无法识别打手机、抽烟等属于人-物交互危险行为的问题,在基于骨骼点的人体行为识别任务中引入目标检测机制,提出基于深度学习的人-物交互行为识别算法。首先,采用OpenPose算法进行姿态估计,进而利用行为识别方法获取初始行为类别;其次,针对传统方法丢失背景和语义信息的问题,使用YOLOv3算法检测感兴趣物体,获得类别和位置信息;然后,通过判断人与物体的空间位置关系来表征人-物交互关系;最后,提出决策融合策略,对人的初始行为类别、物体信息、人-物交互关系进行决策融合,得到最终的行为识别结果。以打手机和抽烟行为为例对所提算法进行验证分析,结果表明,所提算法可以对石化场景下人员的危险行为进行准确识别。

Traditional human action recognition algorithms in petrochemical scenarios focus only on human behaviors and cannot recognize other dangerous behaviors prompted by human-object interactions, such as cell phone calls and smoking. To solve this problem, this paper introduces the object detection mechanism in skeleton-based human action recognition task and proposes a recognition algorithm for human-object interaction using deep learning. First, we used the OpenPose algorithm for pose estimation and then employed the action recognition method to obtain the initial action label. Second, to solve the problems of losing background and semantic informations in traditional methods, the YOLOv3 algorithm was used to detect the objects of interest and obtain their category and location informations. Then, we characterized the human-object interaction relationship by determining the spatial relationship between humans and objects. Finally, a decision-making fusion strategy was proposed, merging the initial action categories of the human, object information, and human-object interaction relationship, to obtain the final action recognition result. Cell phone calls and smoking behaviors were used as examples to verify and analyze the proposed algorithm. Results show that the proposed algorithm can accurately identify dangerous personnel behaviors in a petrochemical scene.