SNNTracker: Online High-speed Multi-Object Tracking with Spike Camera

Multi-object tracking (MOT) is crucial for applications such as autonomous driving and robotics, yet traditional image-based methods struggle in high-speed scenarios due to motion blur and temporal gaps caused by low frame rates. Spike cameras, with their ability to continuously record spatiotemporal signals, overcome these limitations. However, existing spike-based methods often rely on intermediate image reconstruction or discrete clustering, which limits their real-time performance and temporal continuity. To address this, we propose SNNTracker, the first fully spiking neural network (SNN)-based MOT algorithm tailored for spike cameras. SNNTracker integrates a dynamic neural field (DNF)-based attention mechanism for target detection and a winner-take-all (WTA)-based tracking module with online spike-timing-dependent plasticity (STDP) for adaptive learning of object trajectories. By directly processing spike streams without reconstruction, SNNTracker reduces latency, computational overhead, and dependency on image quality, making it ideal for ultra-high-speed environments. It maintains robust, continuous tracking even under occlusions, severe lighting variations, or temporary object disappearance, by leveraging SNN-estimated motion predictions and long-term online clustering. We construct three types of spike-camera MOT datasets covering dense and sparse annotations across diverse real-world scenarios, including camera ego-motion, deformable and ultra-fast motion (up to 2600 RPM), occlusion, indoor/outdoor lighting changes, and low-visibility tracking. Extensive experiments demonstrate that SNNTracker consistently outperforms state-of-the-art MOT methods—both ANN- and SNN-based—achieving MOTA scores above 96% and up to 100% in many sequences. Our results highlight the advantages of spike-driven SNNs for low-latency, high-speed, and label-free multi-object tracking, advancing neuromorphic vision for real-time perception.