Distant Object Localisation from Noisy Image Segmentation Sequences
Researchers have developed new methods for accurately localizing distant objects using noisy image segmentation sequences, a crucial task for safety-critical applications like drone-based wildfire monitoring. The proposed solutions, based on multi-view triangulation and particle filters, can estimate object shape and uncertainty without requiring specialized sensor configurations or extensive 3D scene reconstruction. Tested through 3D simulations and real-world drone footage, the methods integrate with existing image segmentation models and onboard computational resources to create a reliable monitoring system. AI
IMPACT Enhances the reliability of AI-driven monitoring systems for critical infrastructure and environmental safety.