Video Object Detection

Video object detection (VOD) aims to identify and locate objects within video sequences, a task more complex than still-image object detection due to the added temporal dimension. Current research emphasizes efficient algorithms, often based on one-stage detectors like YOLO and improved transformer architectures, that leverage temporal consistency and context across frames to improve accuracy while minimizing computational cost. These advancements are crucial for real-time applications such as autonomous driving, surveillance, and medical image analysis, where efficient and accurate object tracking is essential. Furthermore, research is actively exploring techniques to handle challenging conditions like adverse weather, low light, and limited labeled data.