DAODiS: Domain-Adapted Object Detection in Surveillance Videos

In video surveillance applications, dealing with domain shifts and unknown classes is critical but still open issue. Recently, many object detection methods have been proposed using CNN such as YOLO, Faster-RCNN, and Retina-net . However, the performance of these methods are very sensitive against domain shifts. We aim to tackle the challenge for developing more accurate object detection with fewer annotations.