Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways

• URL: http://arxiv.org/abs/2302.11283v1
• Date: Wed, 22 Feb 2023 11:00:34 GMT
• Title: Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways
• Authors: Yu Guo, Ryan Wen Liu, Jingxiang Qu, Yuxu Lu, Fenghua Zhu, Yisheng Lv
• Abstract summary: The automatic identification system (AIS) and video cameras have been widely exploited for vessel traffic surveillance in inland waterways. We propose a deep learning-enabled asynchronous trajectory matching method (named DeepSORVF) to fuse the AIS-based vessel information with the corresponding visual targets. In addition, by combining the AIS- and video-based movement features, we also present a prior knowledge-driven anti-occlusion method to yield accurate and robust vessel tracking results.
• Score: 12.7548343467665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The automatic identification system (AIS) and video cameras have been widely exploited for vessel traffic surveillance in inland waterways. The AIS data could provide the vessel identity and dynamic information on vessel position and movements. In contrast, the video data could describe the visual appearances of moving vessels, but without knowing the information on identity, position and movements, etc. To further improve vessel traffic surveillance, it becomes necessary to fuse the AIS and video data to simultaneously capture the visual features, identity and dynamic information for the vessels of interest. However, traditional data fusion methods easily suffer from several potential limitations, e.g., asynchronous messages, missing data, random outliers, etc. In this work, we first extract the AIS- and video-based vessel trajectories, and then propose a deep learning-enabled asynchronous trajectory matching method (named DeepSORVF) to fuse the AIS-based vessel information with the corresponding visual targets. In addition, by combining the AIS- and video-based movement features, we also present a prior knowledge-driven anti-occlusion method to yield accurate and robust vessel tracking results under occlusion conditions. To validate the efficacy of our DeepSORVF, we have also constructed a new benchmark dataset (termed FVessel) for vessel detection, tracking, and data fusion. It consists of many videos and the corresponding AIS data collected in various weather conditions and locations. The experimental results have demonstrated that our method is capable of guaranteeing high-reliable data fusion and anti-occlusion vessel tracking.

Related papers

• Image and AIS Data Fusion Technique for Maritime Computer Vision Applications [1.482087972733629]
  We develop a technique that fuses Automatic Identification System (AIS) data with vessels detected in images to create datasets. Our approach associates detected ships to their corresponding AIS messages by estimating distance and azimuth. This technique is useful for creating datasets for waterway traffic management, encounter detection, and surveillance.
  arXiv  Detail & Related papers  (2023-12-07T20:54:49Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data [2.094022863940315]
  This study introduces a 1D CNN-LSTM architecture-based framework for track association. The proposed framework takes the marine vessel's location and motion data collected through the Automatic Identification System (AIS) as input and returns the most likely vessel track as output in real-time.
  arXiv  Detail & Related papers  (2023-03-24T15:26:49Z)
• SimuShips -- A High Resolution Simulation Dataset for Ship Detection with Precise Annotations [0.0]
  State-of-the-art obstacle detection algorithms are based on convolutional neural networks (CNNs) SimuShips is a publicly available simulation-based dataset for maritime environments.
  arXiv  Detail & Related papers  (2022-09-22T07:33:31Z)
• Online Deep Clustering with Video Track Consistency [85.8868194550978]
  We propose an unsupervised clustering-based approach to learn visual features from video object tracks. We show that exploiting an unsupervised class-agnostic, yet noisy, track generator yields to better accuracy compared to relying on costly and precise track annotations.
  arXiv  Detail & Related papers  (2022-06-07T08:11:00Z)
• Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial Vehicles in Congested Harbors and Waterways [9.270928705464193]
  This work is the first attempt to detect and estimate distances to unknown objects from long-range visual data captured with conventional RGB cameras and auxiliary absolute positioning systems (e.g. GPS) The simulation results illustrate the accuracy and efficacy of the proposed method for visually aided navigation of vessels assisted by UAV.
  arXiv  Detail & Related papers  (2021-08-09T08:15:17Z)
• DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection [78.04869214450963]
  We propose a novel dynamic temporal-temporal network (DSNet) for more effective fusion of temporal and spatial information. We show that the proposed method achieves superior performance than state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-12-09T06:42:30Z)
• A Spatio-temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data [5.453186558530502]
  Tracking objects moving in real-time in a dynamic threat environment is important in national security and surveillance system. To locate the anomalous pattern of movements, one needs to have an accurate data association algorithm. We develop atemporal approach for tracking maritime vessels as the vessel's location and motion posing observations are collected by an Automatic Identification System.
  arXiv  Detail & Related papers  (2020-10-29T20:11:38Z)
• A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View [93.23947591795897]
  In this paper, we strive to tackle the challenges and automatically understand the crowd from the visual data collected from drones. To alleviate the background noise generated in cross-scene testing, a double-stream crowd counting model is proposed. To tackle the crowd density estimation problem under extreme dark environments, we introduce synthetic data generated by game Grand Theft Auto V(GTAV)
  arXiv  Detail & Related papers  (2020-09-29T01:48:24Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• SoDA: Multi-Object Tracking with Soft Data Association [75.39833486073597]
  Multi-object tracking (MOT) is a prerequisite for a safe deployment of self-driving cars. We propose a novel approach to MOT that uses attention to compute track embeddings that encode dependencies between observed objects.
  arXiv  Detail & Related papers  (2020-08-18T03:40:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.