M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV
| Autor: | Guangzhong Liu, Gongxing Wu, Dalei Qiao, Feng Dong, Jun Zhang, Qiangyong Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Matching (statistics)
Computer Networks and Communications Computer science lcsh:TK7800-8360 02 engineering and technology Kinematics Tracking (particle physics) maritime surveillance 0202 electrical engineering electronic engineering information engineering multimodel and multicue (M3C) Computer vision Electrical and Electronic Engineering business.industry Deep learning unmanned surface vessels 020208 electrical & electronic engineering lcsh:Electronics deep learning Filter (signal processing) Frame rate Pipeline (software) Hardware and Architecture Control and Systems Engineering tracking by detection Signal Processing Metric (mathematics) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Electronics, Vol 8, Iss 7, p 723 (2019) Electronics Volume 8 Issue 7 |
| ISSN: | 2079-9292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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