Exploring and Analyzing the Different Models of Video Object Detection

Video object detection aims to identify the objects that appear in the frame of video streams.Key challenges in video object detection include the degradation of video frames caused by fast motion,occlusion,scale variations,and the need for real-time processing. To achieve these challenges, we have analyzed var-ious approaches, such as two-stage detectors(e.g.,Faster R-CNN or CNN) and one-stage detectors(e.g.,CenterNet),which adapt to video data, Sequence Level Semantics Aggregation(SELSA) module (working on cluster of frames). The motivation for using deep learning, particularly CNN s.in this system is their ability to handle complex scenes,lighting variations, feature extraction,and different object types. The versatile and efficient algorithm for video object detection,offers real-time performance,accuracy, and the ability to detect multiple objects in a single pass.Hence this highlights the importance of video object detection and the advancements in technology that make it possible to tackle real-world video analysis tasks effectively.