Dataset for Human Activity Recognition

Published: 1 May 2024| Version 1 | DOI: 10.17632/67bbcr5ssp.1
mubashar saddique, Iqra Muneer


Recognizing actions through visual cues poses a particularly formidable challenge within the domains of computer vision and pattern recognition. A specific practical application of this challenge involves the swift identification of instances of physical altercations, such as fights, captured by surveillance cameras in public spaces and correctional facilities. Nevertheless, the field of action recognition has primarily focused its efforts on relatively straightforward actions, such as clapping, walking, wrestling, and jogging. In contrast, the identification of specific events with immediate practical applications, such as cellphone snatching, common fighting, and running behavior in general, has received comparatively less attention. The ability to detect such events could prove immensely valuable in various video surveillance contexts, including prisons, psychiatric facilities, and violence detection based on human activity. As a result, there is a growing interest in the development of algorithms and gold-standard benchmarks geared toward detecting instances of violence and abnormal behaviors based on human activity recognition problems. To address this limitation, this study presents a substantial benchmark dataset comprising of totaling 6,048 video frames, featuring 2016 cases of cellphone snatching, 2016 cases of fighting, and 2016 cases of running, each showcasing five different forms of human activities. Our proposed dataset will be made publicly available to foster and promote research in human action recognition behaviors, including the development of robbery detection systems, human movement detection systems, safety systems, theft detection systems, and anomaly detection in automatic surveillance cameras.



University of Engineering and Technology


Computer Vision