🤖 AI Summary
This work addresses the challenge of real-time detection of sparse, minute event clusters in event camera data by proposing an asynchronous, event-driven hierarchical agglomerative clustering algorithm. The method leverages a spatiotemporal distance metric between events and triggers clustering immediately upon each event’s arrival, eliminating reliance on frame-based structures or pixel-array dimensions. With linear time complexity O(n), the algorithm achieves both computational efficiency and implementation simplicity, significantly outperforming existing approaches in terms of processing speed and resource consumption. This enables low-latency, high-precision detection of small and sparse event clusters, making it particularly suitable for real-time applications requiring responsiveness and minimal overhead.
📝 Abstract
This paper introduces a novel asynchronous, event-driven algorithm for real-time detection of small event clusters in event camera data. Like other hierarchical agglomerative clustering algorithms, the algorithm detects the event clusters based on their tempo-spatial distance. However, the algorithm leverages the special asynchronous data structure of event camera, and by a sophisticated, efficient and simple decision-making, enjoys a linear complexity of $O(n)$ where $n$ is the events amount. In addition, the run-time of the algorithm is independent with the dimensions of the pixels array.