Future Aspects in Human Action Recognition: Exploring Emerging Techniques and Ethical Influences

TL;DR

This paper reviews emerging techniques in human action recognition, emphasizing the challenges of temporal analysis, dataset limitations, synthetic data generation, and ethical considerations in deploying these technologies.

Contribution

It explores new methods involving next-generation sensors, synthetic video generation, and reinforcement learning to improve action recognition and addresses ethical issues related to human factors.

Findings

Emerging sensor data can enhance temporal analysis in action recognition.

Synthetic videos and reinforcement learning can mitigate dataset limitations.

Ethical concerns are significant in deploying human action recognition technologies.

Abstract

Visual-based human action recognition can be found in various application fields, e.g., surveillance systems, sports analytics, medical assistive technologies, or human-robot interaction frameworks, and it concerns the identification and classification of individuals' activities within a video. Since actions typically occur over a sequence of consecutive images, it is particularly challenging due to the inclusion of temporal analysis, which introduces an extra layer of complexity. However, although multiple approaches try to handle temporal analysis, there are still difficulties because of their computational cost and lack of adaptability. Therefore, different types of vision data, containing transition information between consecutive images, provided by next-generation hardware sensors will guide the robotics community in tackling the problem of human action recognition. On the other hand, while there is a plethora of still-image datasets, that researchers can adopt to train new artificial intelligence models, videos representing human activities are of limited capabilities, e.g., small and unbalanced datasets or selected without control from multiple sources. To this end, generating new and realistic synthetic videos is possible since labeling is performed throughout the data creation process, while reinforcement learning techniques can permit the avoidance of considerable dataset dependence. At the same time, human factors' involvement raises ethical issues for the research community, as doubts and concerns about new technologies already exist.