Adjust Sample Imbalance and Exclude Similar Object in Underwater Object Tracking

Abstract

Visual tracker includes network and post-processing. Despite the color distortion and low contrast of underwater images, advanced trackers can still be very competitive in underwater object tracking because deep learning empowers the networks to discriminate the appearance features of the target. However, underwater object tracking also faces another problem. Underwater targets such as fish and dolphins, usually appear in groups, and creatures of the same species usually have similar expressions of appearance features, so it is challenging to distinguish the weak differences characteristics only by the network itself. The existing detection-based post-processing only reflects the results of single frame detection, but cannot locate real targets among similar targets. In this paper, we propose a new post-processing strategy based on motion, which uses Kalman filter (KF) to maintain the motion information of the target and exclude similar targets around. Specifically, we use the KF predicted box and the candidate boxes in the response map and their confidence to calculate the candidate location score to find the real target. Our method does not change the network structure, nor does it perform additional training for the tracker. It can be quickly applied to other tracking fields with similar target problem. We improved SOTA trackers based on our method, and proved the effectiveness of our method on UOT100 and UTB180. The AUC of our method for OSTrack on similar subsequences is improved by more than 3% on average, and the precision and normalization precision are improved by more than 3.5% on average. It has been proved that our method has good compatibility in dealing with similar target problems and can enhance performance of the tracker together with other methods. More details can be found in: https://github.com/LiYunfengLYF/KF_in_underwater_trackers.