PUTrack: Improved Underwater Object Tracking via Progressive Prompting

Qiuyang Zhang, Wei Song, Cong Liu, Minghua Zhang

Abstract: Existing underwater tracking methods can be categorized into two paradigms: first, “enhance-then-track”—first enhancing the quality of the input image, then employing an open-air tracker; second, “track-then-process”—initially using an open-air tracker, followed by calibrating the prediction box. These methods that lack unified objectives among the modules impair tracking performance. To overcome this, we propose a novel end-to-end framework called prompting underwater tracking (PUTrack). It adapts the open-air tracker to the scenario-specific (underwater) tracking task by deploying a set of underwater prompters at the lateral side of an existing open-air tracker, and injecting the generated prompts layer-by-layer into the encoder. Experiments on various underwater tracking datasets demonstrate that the method significantly improves the underwater performance of the tracker by introducing only 0.6 M trainable parameters (0.4% of total parameters). Moreover, to drive the development of underwater tracking, we construct a high quality underwater tracking dataset that is manually annotated with 139 k frames, which exceeds the total number of frames in previous underwater tracking datasets. It provides 90 test sets rich in challenge properties and 200 training sets of diverse kinds.