Video Object Detection and Human Action Recognition: Techniques, Challenges, and Future Trends

Video object detection and human action recognition have become essential components of modern computer vision systems. These techniques are widely utilized in scenarios such as traffic monitoring, industrial safety, and public surveillance. However, challenges like motion blur, occlusion, and video defocus hinder the accuracy and reliability of detection systems. This paper provides a comprehensive review of video object detection techniques, covering frame-based approaches, one-stage and two-stage detection algorithms, and mixed-stage methods. It also examines commonly used datasets, including ImageNet VID and YouTube-Bounding Boxes, to highlight their strengths and limitations. Furthermore, advancements in object detection, including deep learning methods like YOLO and Vision Transformers, are discussed. By identifying challenges such as real-time detection in resource-constrained environments and ethical concerns like data privacy, this study explores emerging trends and future directions in video detection systems. The findings aim to guide researchers and practitioners toward developing efficient, robust, and ethical video object detection solutions.