Parallel Three-Branch Correlation Filters for Complex Marine Environmental Object Tracking Based on a Confidence Mechanism

Abstract

Marine object tracking is critical for search and rescue activities in the complex marine environment. However, the complex marine environment poses a huge challenge to the effect of tracking, such as the variability of light, the impact of sea waves, the occlusion of other ships, etc. Under these complex marine environmental factors, how to design an efficient dynamic visual tracker to make the results accurate, real time and robust is particularly important. The parallel three-branch correlation filters for complex marine environmental object tracking based on a confidence mechanism is proposed by us. The proposed tracker first detects the appearance change and position change of the object by constructing parallel three-branch correlation filters, which enhances the robustness of the correlation filter model. Through the weighted fusion of response maps, the center position of the object is accurately located. Secondly, the Gaussian-triangle joint distribution is used to replace the original Gaussian distribution in the training phase. Finally, a verification mechanism of confidence metric is embedded in the filter update section to analyze the tracking effect of the current frame, and to update the filter sample from verification result. Thus, a more accurate correlation filter is trained to prevent model drift and achieve a good tracking effect. We found that the effect of various interferences on the filter is effectively reduced by comparing with other trackers. The experiments prove that the proposed tracker can play an outstanding role in the complex marine environment.

Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

“…Researchers have introduced advanced correlation filtering and deep learning methods for ship tracking in sea surface surveillance videos in recent years. Correlation filtering [60] is renowned for its fast tracking speed. However, most of its success is in single target tracking due to defects in multitarget tracking and the fact that extracted A generative model tracking pipeline that consists of a particle filter [57], a Kalman filter [58], and a meanshift algorithm [59] ignores correlation with features having a sea background and other nontargets while ship tracking, which leads to low accuracy.…”

“…Researchers have introduced advanced correlation filtering and deep learning methods for ship tracking in sea surface surveillance videos in recent years. Correlation filtering [60] is renowned for its fast tracking speed. However, most of its success is in single target tracking due to defects in multitarget tracking and the fact that extracted features are greatly affected by illumination and attitude.…”

Low-Light Enhancer for UAV Night Tracking Based on Zero-DCE++

“…With its compact size and adaptable motion, UAV platforms have attracted a lot of attention and applications for visual target tracking, a component of computer vision. Visual target tracking paired with UAVs opens up a larger range of applications, including maritime rescue [1], autonomous landing [2], and selflocalization [3]. Trackers based on correlation filter and trackers based on Siamese have both shown advancement in a number of large-scale benchmarks [4].…”