Deep-Learning-for-Tracking-and-Detection
This collection offers a variety of resources for deep learning-based object detection and tracking. It includes research papers addressing both static and dynamic detection with methods like RCNN, YOLO, SSD, and RetinaNet. The resource set also expands into multi and single object tracking techniques and provides specific datasets for tasks including UAV and microscopy tracking, as well as video segmentation and motion prediction. Comprehensive code repositories and frameworks are available to assist researchers and engineers in achieving efficient and state-of-the-art results in computer vision.
