Video-based Human Action Recognition: Analyzing techniques for recognizing human actions and activities in videos, including temporal modeling and motion analysis

Abstract

Video-based human action recognition is a crucial task in computer vision with applications in surveillance, human-computer interaction, and video indexing. This paper provides a comprehensive review of techniques for recognizing human actions and activities in videos. We focus on temporal modeling and motion analysis, which are key components in achieving high recognition accuracy.

We begin by discussing the challenges of human action recognition, including variability in human movements, occlusions, and complex interactions between multiple individuals. We then review traditional approaches such as optical flow-based methods and discuss their limitations in handling complex actions and scenes.

Next, we delve into deep learning techniques, which have shown remarkable success in human action recognition. We review popular deep learning architectures such as 3D Convolutional Neural Networks (CNNs) and Temporal Convolutional Networks (TCNs) and discuss their effectiveness in capturing temporal information and modeling complex motion patterns.

Furthermore, we explore the integration of attention mechanisms and spatial-temporal graph networks for improved action recognition performance. We also discuss the importance of large-scale datasets such as UCF101 and HMDB51 in training deep learning models for human action recognition.

Finally, we highlight future research directions, including the use of generative adversarial networks (GANs) for data augmentation and the integration of multimodal data (e.g., RGB and depth) for more robust action recognition.