Human Action Recognition With Video Data: Research and Evaluation Challenges

• Published in: IEEE transactions on human-machine systems 2014-10, Vol.44 (5), p.650-663
• Main Authors: Ramanathan, Manoj, Wei-Yun Yau, Eam Khwang Teoh
• Format: Article
• Language: English
• Subjects: Action recognition; anthropometric variations; Anthropometry; camera motion; Cameras; Classification; Clutter; execution rate; Feature extraction; Hidden Markov models; Human; Indexing; Legged locomotion; Recognition; Robustness; Shape; Surveillance; Tasks; Video sequences; view invariance
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2014.2325871

Given a video sequence, the task of action recognition is to identify the most similar action among the action sequences learned by the system. Such human action recognition is based on evidence gathered from videos. It has wide application including surveillance, video indexing, biometrics, telehealth, and human-computer interaction. Vision-based human action recognition is affected by several challenges due to view changes, occlusion, variation in execution rate, anthropometry, camera motion, and background clutter. In this survey, we provide an overview of the existing methods based on their ability to handle these challenges as well as how these methods can be generalized and their ability to detect abnormal actions. Such systematic classification will help researchers to identify the suitable methods available to address each of the challenges faced and their limitations. In addition, we also identify the publicly available datasets and the challenges posed by them. From this survey, we draw conclusions regarding how well a challenge has been solved, and we identify potential research areas that require further work.