面向复杂场景的人物视觉理解技术能够提升社会智能化协作效率，加速社会治理智能化进程，并在服务人类社会的经济活动、建设智慧城市等方面展现出巨大活力，具有重大的社会效益和经济价值。人物视觉理解技术主要包括实时人物识别、个体行为分析与群体交互理解、人机协同学习、表情与语音情感识别和知识引导下视觉理解等，当环境处于复杂场景中，特别是考虑“人物—行为—场景”整体关联的视觉表达与理解，相关问题的研究更具有挑战性。其中，大规模复杂场景实时人物识别主要集中在人脸检测、人物特征理解以及场景分析等，是复杂场景下人物视觉理解技术的重要研究基础；个体行为分析与群体交互理解主要集中在视频行人重识别、视频动作识别、视频问答和视频对话等，是视觉理解的关键行为组成部分；同时，在个体行为分析和群体交互理解中，形成综合利用知识与先验的机器学习模式，包含视觉问答对话、视觉语言导航两个重点研究方向；情感的识别与合成主要集中在人脸表情识别、语音情感识别与合成以及知识引导下视觉分析等方面，是情感交互的核心技术。本文围绕上述核心关键技术，阐述复杂场景下人物视觉理解领域的研究热点与应用场景，总结国内外相关成果与进展，展望该领域的前沿技术与发展趋势。

Public security and social governance is essential to national development nowadays. It is challenged to prevent large-scale riots in communities and various city crimes for spatial and timescaled social governance in corona virus disease 2019(Covid-19) likehighly accurate human identity verification, highly efficient human behavior analysis and crowd flow track and trace. The core of the challenge is to use computer vision technologies to extract visual information in complex scenarios and to fully express, identify and understand the relationship between human behavior and scenes to improve the degree of social administration and governance. Complex scenarios oriented visual technologies recognition can improve the efficiency of social intelligence and accelerate the process of intelligent social governance. The main challenge of human recognition is composed of three aspects as mentioned below:1) the diversity attack derived from mask occlusion attack, affecting the security of human identity recognition; 2) the large span of time and space information has affected the accuracy of multiple ages oriented face recognition (especially tens of millions of scales retrieval); 3) the complex and changeable scenarios are required for the high robustness of the system and adapt to diverse environments. Therefore, it is necessary to facilitate technologies of remote human identity verification related to the high degree of security, face recognition accuracy, human behavior analysis and scene semantic recognition. The motion analysis of individual behavior and group interaction trend are the key components of complex scenarios based human visual contexts. In detail, individual behavior analysis mainly includes video-based pedestrian re-recognition and video-based action recognition. The group interaction recognition is mainly based on video question-and-answer and dialogue. Video-based network can record the multi-source cameras derived individuals/groups image information. Multi-camera based human behavior research of group segmentation, group tracking, group behavior analysis and abnormal behavior detection. However, it is extremely complex that the individual behavior/group interaction is recorded by multiple cameras in real scenarios, and it is still a great challenge to improve the performance of multi-camera and multi-objective behavior recognition through integrated modeling of real scene structure, individual behavior and group interaction. The video-based network recognition of individual and group behavior mainly depends on visual information in related to scene, individual and group captured. Nonetheless, complex scenarios based individual behavior analysis and group interaction recognition require human knowledge and prior knowledge without visual information in common.Specifically, a crowdsourced data application has improved visual computing performance and visual question-and-answer and dialogue and visual language navigation. The inherited knowledge in crowdsourced data can develop a data-driven machine learning model for comprehensive knowledge and prior applications in individual behavior analysis and group interaction recognition, and establish a new method of data-driven and knowledge-guided visual computing. In addition, the facial expression behavior can be recognized as the human facial micro-motions like speech the voice of language. Speech emotion recognition can capture and understand human emotions and beneficial to support the learning mode of human-machine collaboration better. It is important for research to get deeper into the technology of human visual recognition. Current researches have been focused on human facial expression recognition, speech emotion recognition, expression synthesis, and speech emotion synthesis. We carried out about the contexts of complex scenarios based real-time human identification, individual behavior and group interaction understanding analysis, visual speech emotion recognition and synthesis, comprehensive utilization of knowledge and a priori mode of machine learning. The research and application scenarios for the visual ability is facilitated for complex scenarios. We summarize the current situations, and predict the frontier technologies and development trends. The human visual recognition technology will harness the visual ability to recognize relationship between humans, behavior and scenes. It is potential to improve the capability of standard data construction, model computing resources, and model robustness and interpretability further.