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Related Experiment Video

Updated: Oct 27, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

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Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques.

Matthijs H Zwemer^1,2, Herman G J Groot¹, Rob Wijnhoven²

¹Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

Sensors (Basel, Switzerland)

|July 24, 2021

Summary

This study introduces a novel camera-based system for enforcing vessel speed using two cameras and advanced re-identification techniques. The system achieves high accuracy in detecting and tracking vessels, improving maritime safety and compliance.

Keywords:

computer vision application maritime traffic management vessel detection vessel re-identification video surveillance

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Area of Science:

Computer Vision
Maritime Technology
Surveillance Systems

Background:

Current vessel monitoring systems often lack precision in speed enforcement.
Accurate vessel tracking and identification are crucial for maritime safety and regulatory compliance.

Purpose of the Study:

To develop and evaluate a robust camera-based system for vessel-speed enforcement.
To introduce a new dataset for training and validating vessel re-identification models.

Main Methods:

Utilized a dual-camera setup for vessel detection and tracking.
Implemented a re-identification (re-ID) function linking vessels across camera views using multiple bounding-box images.
Introduced the Vessel-reID dataset (2474 vessels, 136,888 images) for system training and evaluation.
Evaluated multiple Convolutional Neural Network (CNN) detector architectures, with SSD512 showing optimal speed-performance.

Main Results:

The SSD512 detector achieved 85.0% Recall@95Precision at 20.1 fps.
Vessel re-ID performance improved significantly with multi-image matching (MGN: 68.9% Rank-1 to 74.5% Rank-1).
Optimizations including travel-time selection and cross-camera matching led to a final performance of 88.9% Rank-1 and 83.5% mAP.

Conclusions:

The proposed camera-based system effectively enforces vessel speed with high accuracy.
The developed Vessel-reID dataset is a valuable resource for advancing vessel re-identification research.
The system demonstrates potential for enhancing maritime surveillance and traffic management.