Visual tracking for underwater sea cucumber via correlation filters

One of the essential techniques for using underwater robots to fish sea cucumbers is that the robots must track sea cucumbers using computer vision technology. Tracking underwater targets is a challenging task due to suspension, water absorption, and light scattering. This study proposed a simple but effective algorithm for sea cucumber tracking based on Kernelized Correlation Filters (KCF) framework. This method tracked the head and tail of the sea cucumber respectively and calculated the scale change according to the distance between the head and tail. The KCF method was improved on three strategies. First of all, the target was searched at the predicted position to improve accuracy. Secondly, an adaptive learning rate updating method based on the detection score of each frame was proposed. Finally, the adaptive size of the histogram of the oriented gradient (HOG) feature was used to balance the accuracy and efficiency. Experimental results showed that the algorithm had good tracking performance.