Visual ship tracking via a hybrid kernelized correlation filter and anomaly cleansing framework

Ship tracking from maritime visual sensing data (namely maritime surveillance videos) provides various kinematic maritime traffic information, which significantly benefits remote maritime traffic controlling and management, off-site law enforcement, etc. But, it is difficult to extract distinct ship visual features when the target ship is sheltered by the neighboring ship in the maritime images (or the images are shot in low visibility condition). To address the difficulty, we proposed a hybrid ship tracking framework via the help of kernelized correlation filter (KCF) and anomaly cleaning models (including curve fitting method and Kalman filter). First, we employed the KCF model to obtain raw ship trajectories in consecutive maritime images. Second, our ship tracker is accurately initialized (to be specific, ground truth ship position in the first frame is employed to initialize the ship tracker). Third, the Kalman filter is introduced to suppress the trivial ship position oscillations in the raw ship trajectories. We verified the proposed framework performance on the four typical maritime scenarios. The experimental results indicate that the proposed ship tracker showed more accurate ship tracking results compared to other four popular ship trackers in terms of average root mean square error (RMSE), mean absolute deviation (MAD) and mean square error (MSE). The research findings can help maritime traffic participants obtain visual on-spot maritime kinematic information, and thus further enhance maritime traffic safety.

Automated summary

A hybrid ship tracking framework utilizing the KCF model and anomaly cleaning models was proposed to address the difficulty of extracting distinct ship visual features from maritime images. Experimental results showed that the proposed framework outperformed other popular ship trackers in terms of accuracy and error metrics, demonstrating its effectiveness in enhancing maritime traffic safety.