In order for cooperative robots ("co-robots") to respond to human behaviors accurately and efficiently in human-robot collaboration, interpretation of human actions, awareness of new situations, and appropriate decision making are all crucial abilities for co-robots. For this purpose, the human behaviors should be interpreted by co-robots in the same manner as human peers. To address this issue, a novel interpretability indicator is introduced so that robot actions are appropriate to the current human behaviors. In addition, the complete consideration of all potential situations of a robot's environment is nearly impossible in real-world applications, making it difficult for the co-robot to act appropriately and safely in new scenarios. This is true even when the pretrained model is highly accurate in a known situation. For effective and safe teaming with humans, we introduce a new generalizability indicator that allows a co-robot to self-reflect and reason about when an observation falls outside the co-robot's learned model. Based on topic modeling and two novel indicators, we propose a new Self-reflective Risk-aware Artificial Cognitive (SRAC) model. The co-robots are able to consider action risks and identify new situations so that better decisions can be made. Experiments both using real-world datasets and on physical robots suggest that our SRAC model significantly outperforms the traditional methodology and enables better decision making in response to human activities.
Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require an automated recognition of high-level activities, composed of multiple simple (or atomic) actions of persons. This article provides a detailed overview of various state-of-the-art research papers on human activity recognition. We discuss both the methodologies developed for simple human actions and those for high-level activities. An approach-based taxonomy is chosen that compares the advantages and limitations of each approach. Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article. Space-time volume approaches and sequential approaches that represent and recognize activities directly from input images are discussed. Next, hierarchical recognition methodologies for high-level activities are presented and compared. Statistical approaches, syntactic approaches, and description-based approaches for hierarchical recognition are discussed in the article. In addition, we further discuss the papers on the recognition of human-object interactions and group activities. Public datasets designed for the evaluation of the recognition methodologies are illustrated in our article as well, comparing the methodologies' performances. This review will provide the impetus for future research in more productive areas. Understanding human behaviors is a challenging problem in computer vision that has recently seen important advances. Human behavior understanding combines image and signal processing, feature extraction, machine learning, and 3-D geometry. Application scenarios range from surveillance to indexing and retrieval, from patient care to industrial safety and sports analysis. Given the broad set of techniques used in video-based behavior understanding and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguous key terms, and discuss links among fundamental building blocks ranging from human detection to action and interaction recognition. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects of video-based human behavior understanding, available datasets for experimentation and comparisons, and important open research issues.