Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking

2026-03-09

AI Summary

This work addresses the vulnerability of single-vessel tracking from fixed coastal platforms to sensor degradation—specifically, camera performance under adverse lighting and occlusion, and LiDAR limitations at long ranges or with weak reflectivity. To mitigate these issues, the authors propose an adaptive multi-sensor fusion particle filter grounded in information gain (entropy reduction), which dynamically schedules a shore-based 3D LiDAR and an elevated camera to select the optimal sensing configuration within each fusion window. The approach maintains high tracking accuracy while substantially reducing computational overhead by avoiding continuous multi-stream processing. Real-world maritime experiments demonstrate that the system prioritizes LiDAR in near-field scenarios and switches to camera-based tracking in the far field, achieving both continuity and robustness. This provides an efficient and practical tracking baseline for resource-constrained maritime surveillance.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Robust single-vessel tracking from fixed coastal platforms is hindered by modality-specific degradations: cameras suffer from illumination and visual clutter, while LiDAR performance drops with range and intermittent returns. We present a heterogeneous multi-sensor fusion particle-filter tracker that incorporates an information-gain (entropy-reduction) adaptive sensing policy to select the most informative configuration at each fusion time bin. The approach is validated in a real maritime deployment at the CMMI Smart Marina Testbed (Ayia Napa Marina, Cyprus), using a shore-mounted 3D LiDAR and an elevated fixed camera to track a rigid inflatable boat with onboard GNSS ground truth. We compare LiDAR-only, camera-only, all-sensors, and adaptive configurations. Results show LiDAR dominates near-field accuracy, the camera sustains longer-range coverage when LiDAR becomes unavailable, and the adaptive policy achieves a favorable accuracy-continuity trade-off by switching modalities based on information gain. By avoiding continuous multi-stream processing, the adaptive configuration provides a practical baseline for resilient and resource-aware maritime surveillance.

Problem

• single-vessel tracking
• modality-specific degradations
• camera-LiDAR fusion
• maritime surveillance
• sensor reliability

Innovation

• adaptive sensor selection
• entropy-driven policy
• multi-sensor fusion
• particle filter
• maritime vessel tracking