RISTrack: Robust Infrared Ship Tracking With Modified Appearance Feature Extraction and Matching Strategy

Abstract

Infrared (IR) ship tracking is becoming increasingly important in various applications. However, it remains a challenging task as the information that can be obtained from infrared images is limited. Aiming at enhancing IR ship tracking accuracy, we propose an innovative approach by presenting feature integration module (FIM) and backup matching module (BMM). FIM takes appearance feature, complete intersection over union (CIoU), and motion direction metrics into account. Regarding appearance feature extraction, an end-to-end characteristic learning strategy with a cross-guided multi-granularity fusion network is proposed to obtain more integral appearance features and enhance re-identification accuracy, which helps to distinguish individual IR ship targets better. Besides, a backup matching strategy is then used to match the unmatched tracks and detections after cascaded matching. Virtual trajectories are generated for the matched tracks to optimize parameters by parameter optimization module (POM). The accumulation of errors caused by the lack of observations in the Kalman filter is reduced. Thus, the position of IR ships can be estimated more accurately, and more robust IR ship tracking can be achieved. In addition, we present a sequential frame IR ship tracking dataset, providing the first public benchmark for testing IR ship tracking performance. Experimental results indicate that the MOTA, MOTP and IDs of the proposed method are 73.441, 80.826, and 32, respectively, outperforming other state-of-the-art methods. This demonstrates the superior robustness of the proposed method, particularly when the IR ships are occluded or the target texture information is lacking. Our dataset is available at https://github.com/echo-sky/SFIST.