An Improved Methodology for Moving Object Tracking and Detection in Video Frames

Abstract

Surveillance and security, autonomous driving, robotics, and medical imaging are just some of the fields that might benefit from computer vision tasks like monitoring and identifying moving objects in video frames. Several methods have been proposed over time as possible answers to the challenges of monitoring and identifying moving objects. Occlusion, lighting changes, blurring in motion, and distracting backgrounds are all examples of these challenges. Algorithms have improved in accuracy, robustness, and efficiency thanks to recent breakthroughs in fields including deep learning, multi-modal data fusion, attention mechanisms, online learning, unsupervised learning, motion segmentation, graph-based techniques, and reinforcement learning. Tracking and identifying moving targets has many potential uses across many fields, and it will only grow in significance as technology develops. However, there are still problems that need to be fixed, including real-time processing limitations, occlusion, motion blur, sensitivity to lighting changes, and scene complexity management. These are only a few examples of the problems that still require fixing. This research provides an overview of the challenges, current solutions, practical applications, and potential future developments associated with identifying and tracking moving objects in video. Future research opportunities are also highlighted in this overview.