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

1
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
2
Jiangsu Maritime Institute, Nanjing 211100, China
3
College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
4
College of Information Engineering, Shaoyang University, Shaoyang 422000, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 723; https://doi.org/10.3390/electronics8070723
Received: 23 May 2019 / Revised: 20 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
(This article belongs to the Special Issue Smart, Connected and Efficient Transportation Systems)
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Abstract

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. View Full-Text
Keywords: maritime surveillance; tracking by detection; multimodel and multicue (M3C); deep learning; unmanned surface vessels maritime surveillance; tracking by detection; multimodel and multicue (M3C); deep learning; unmanned surface vessels
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Qiao, D.; Liu, G.; Zhang, J.; Zhang, Q.; Wu, G.; Dong, F. M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV. Electronics 2019, 8, 723.

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