Underwater Object Tracking Based on Error Self-correction

AbstractUnderwater object tracking is more challenging than tracking on land due to the image distortion. Underwater illumination changes, motion blur and other problems will greatly degrade the performance of various tracking methods, especially when using underwater cameras. To overcome these limitations, we proposed an underwater tracking method based on error self-correction. We combine the SiamFC tracker and correlation filter trackers into one framework. In this framework, the correlation filter tracker is used to guide the update of SiamFC tracker. In addition, this framework contains a multi-peak suppression module, which comprehensively improves the tracking accuracy of our tracker in underwater scenarios. Experimental results on the underwater dataset we established demonstrate that the success rate of the AUC criterion is 68.2%, which has a 11.4% improvement over the baseline tracker. Also, our proposed algorithm achieves a 12.6% improvement over the conventional correlation filter tracker BACF, especially in challenging scenarios.KeywordsUnderwater object trackingError self-correctionSiamFCCorrelation filter