目的 为实时监测快递分拣过程中粗暴对待包裹的行为，设计一款基于树莓派+EdgeTPU的快递暴力分拣人体行为视觉识别系统。方法 基于TensorFlow深度学习框架，使用PoseNet模型实时采集人体姿态数据，通过LSTM+Attention模型实现人体动作识别，结合MobileSSD进行场景识别，最终实现暴力分拣人体行为视觉识别。结果 实验证明，文中提出的视觉识别方法可以实现暴力分拣5种动作的快速、准确识别，LSTM+Attention人体动作分类模型的测试准确率达到了80%。结论 基于该方法构建的嵌入式暴力分拣行为识别系统，可以实时监测快递分拣中粗暴对待包裹的行为，并实时地告警。

In order to monitor the behavior of rough handling of parcels in the process of express sorting in real time, a visual recognition system of human behavior in violent express sorting based on Raspberry Pi + Edge TPU was designed. Based on TensorFlow deep learning framework, PoseNet model is used to collect human posture data in real time, LSTM + Attention model is used to realize human action recognition, and Mobile SSD is combined to perform scene recognition so as to realize human behavior visual recognition of violent express sorting. Experiments show that the visual recognition method proposed in this paper can realize the fast and accurate recognition of five kinds of violent sorting activity, and the test accuracy of LSTM + Attention human action classification model reaches 80%. The embedded violent sorting behavior recognition system based on this method can monitor the behavior of rough handling parcels in express sorting in real-time, and give real-time warning.