A Comprehensive Study on Multiple Object Detection and Tracking in Videos Under Complex Environments Using Advanced Deep Learning Models

Multiple Object detection and tracking is a predominant area in computer vision and has numerous applications, including healthcare monitoring, autonomous driving, and anomaly detection. The proposed study is focused on multiple object detection and tracking under complex environments using advanced deep learning frameworks. Multiple object detection from videos under complex environments presents several challenges due to the dynamic and unpredictable nature of real-world conditions. The study summarizes the state-of-the-art methods, including important algorithms, datasets, and assessment measures, making it a fundamental resource for scholars and practitioners. It also highlights the shortcomings and difficulties in current solutions, offering information about areas that need more research. In conclusion, this study highlights new developments and approaches for the future, promoting the creation of innovative models and frameworks that can tackle outstanding problems in object tracking and detection.