🤖 AI Summary
This work addresses the degradation of tracking performance in asynchronous multi-sensor systems, where consecutive missed detections from high-frequency sensors can disrupt trajectories maintained by low-frequency sensors—a limitation rooted in the conventional assumption of globally uniform observability. To overcome this, the authors introduce a DetectorContext abstraction within the Stone Soup framework, which dynamically models detection probability and clutter intensity as functions of both target state and perceptual context during hypothesis generation. This context-aware sensor modeling enhances trajectory stability in asynchronous, partially overlapping sensor configurations without requiring modifications to the update equations of existing probabilistic trackers. Experimental results on radar–LiDAR asynchronous data demonstrate significant improvements in HOTA and GOSPA metrics, confirming restored fusion performance without introducing spurious tracks.
📝 Abstract
Multi-sensor tracking in the real world involves asynchronous sensors with partial coverage and heterogeneous detection performance. Although probabilistic tracking methods permit detection probability and clutter intensity to depend on state and sensing context, many practical frameworks enforce globally uniform observability assumptions. Under multi-rate and partially overlapping sensing, this simplification causes repeated non-detections from high-rate sensors to erode tracks visible only to low-rate sensors, potentially degrading fusion performance.
We introduce DetectorContext, an abstraction for the open-source multi-target tracking framework Stone Soup. DetectorContext exposes detection probability and clutter intensity as state-dependent functions evaluated during hypothesis formation. The abstraction integrates with existing probabilistic trackers without modifying their update equations. Experiments on asynchronous radar-lidar data demonstrate that context-aware modeling restores stable fusion and significantly improves HOTA and GOSPA performance without increasing false tracks.