DAODiS: Domain-Adapted Object Detection in Surveillance Videos
Welcome to DAODiS!!
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.
- Domain-shift such as night scenes and thermal scenes
This schedule is a tentative schedule.
- Dec. 1, 2019: ad-hoc website open.
- Feb. 5, 2020: Dataset for development available.
- May. 1 2020: Dataset for evaluation available.
- Jul. 1, 2020: Deadline for the submission of results.
- Jul. 15, 2020: Notification of participation.
- Aug. 1, 2020: Submissions of Contest Results and submission of a 2-3 pagereport for copying and distribution to the participants.
- Sep. 22-25, 2020: Results of the competition are announced at the conference
- Atsushi Shimada (Kyushu Univ.)
- Janusz Konrad (Boston Univ.)
- Vincent Charvillat (ENSEEIHT)
- Tsubasa Minematsu (Kyushu Univ.)
- Takashi Shibata (NEC Corp.)
- Yasutomo Kawanishi (Nagoya Univ.)