Abstract: Within the current domain of space situational awareness, the traditional frame based visual sensors have certain limitations in detection and tracking of swiftly moving space point objects, struggling to meet the escalating demands of tasks. Thus, neuromorphic event based cameras, with their high temporal resolution and dynamic range, emerge as a focal point of research. A space point object tracking method is proposed based on asynchronous event stream. Initially, due to the substantial noise present in the raw event stream data, noise is filtered out through a single layer spiking neuron, yielding potential objects. Subsequently, these candidates are persistently tracked using nearest neighbor motion trajectory association, which elucidates the movement trajectories of each. Ultimately, spurious candidates are eliminated through feature weight false alarm filtering, retaining the genuine motion trajectories of space point objects. During the experimental phase, the CeleX V event camera is employed to measure event data and the public space object event dataset (EBSSA dataset) is utilized to substantiate the efficacy of the proposed algorithm. Notably, in terms of sensitivity and Informedness content, it possesses distinct advantages over the event based space object tracking methods mentioned within the literature, affirming that the asynchronous event stream based tracking can accurately detect one or multiple space point objects from raw event stream data and capture their motion trajectories.

Key words: space point object, event stream data, spiking neuron, object tracking