M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

Electronics

Guangzhong Liu¹  Dalei Qiao¹  Feng Dong¹  Gongxing Wu²  Jun Zhang³  Qiangyong Zhang³ 

[1] College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China;College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China;Jiangsu Maritime Institute, Nanjing 211100, China;

关键词: maritime surveillance;    tracking by detection;    multimodel and multicue (M³C);    deep learning;    unmanned surface vessels;   

DOI  :  10.3390/electronics8070723

来源: DOAJ

【 摘 要 】

It is crucial for unmanned surface vessels (USVs) to detect and track surrounding vessels in real time to avoid collisions at sea. However, the harsh maritime environment poses great challenges to multitarget tracking (MTT). In this paper, a novel tracking by detection framework that integrates the multimodel and multicue (M³C) pipeline is proposed, which aims at improving the detection and tracking performance. Regarding the multimodel, we predicted the maneuver probability of a target vessel via the gated recurrent unit (GRU) model with an attention mechanism, and fused their respective outputs as the output of a kinematic filter. We developed a hybrid affinity model based on multi cues, such as the motion, appearance, and attitude of the ego vessel in the data association stage. By using the proposed ship re-identification approach, the tracker had the capability of appearance matching via metric learning. Experimental evaluation of two public maritime datasets showed that our method achieved state-of-the-art performance, not only in identity switches (IDS) but also in frame rates.

【 授权许可】

Unknown