EBSTracker: event-based space multi-object tracking with bidirectional self-attention and multi-stage data association

Event cameras are preferred for space object tracking due to their high temporal resolution and ability to capture dim light, fast-moving objects, and other challenging space objects. However, existing event trackers still use conventional tracking methods based on object textures, which may not be robust enough for challenging scenarios. To address this, we propose the Event-Based Space multi-object Tracker (EBSTracker), which integrates a bidirectional self-attention network and a multi-stage data association network. The bidirectional self-attention network enhances feature representation for tiny objects, while the multi-stage data association network uses the Noise Scale Adaptive (NSA) Kalman filter and Generalized Intersection over Union (GIoU) metric to predict trajectory positions, improving tracking robustness. Experiments on two large-scale datasets have demonstrated the effectiveness and robustness of EBSTracker, achieving state-of-the-art (SOTA) performance in challenging scenarios with tiny moving objects. This has advanced event-based space multi-object tracking technology.