Underwater object tracking strategy via multi-scale retinex and partial least squares analysis

Underwater object tracking is one of the most essential and fundamental tasks in ocean investigations recent years. In this paper, we try to capture multi-scale retinex (MSR) model as well as the partial least square (PLS) analysis for underwater object tracking. We first make use of multi-scale retinex model to evolve and enhance the partial color constancy from the underwater video sequences, which could provide a versatile automatic strategy to simultaneous sharpening, dynamic range compression and color rendition. The partial least square analysis is further taken to capture the trajectories of underwater objects by learning a set of underwater appearance models for adaptive discriminative object representation. The proposed object tracking algorithm exploits both the ground truth appearance information of the labeled underwater object in the first frame and the image sequences observed online, thereby alleviating the tracking drift problem caused by modeling update.