High-Throughput Event-Based Feature Detection and Tracking on an Embedded CPU

Event cameras provide asynchronous visual sensing, making them a compelling modality for agile edge AI systems. For practical deployment, event-based vision algorithms must sustain sufficiently high throughput to keep pace with the incoming event stream. Yet existing approaches often fail to do so, as they are rarely designed in close consideration of the underlying sensor and compute hardware. We propose a new event-based feature detection and tracking algorithm that operates at high throughput on an embedded CPU, without GPU acceleration. Our pipeline is motion-defined: it activates only when sufficient motion is detected and dynamically adapts its computational load to the observed motion. At its core is a novel slice-parallel formulation of event-based corner detection and tracking that operates directly on live event streams. The pipeline is enabled by our Motion-Defined Concurrent Slices architecture, which efficiently processes event data and maximizes utilization of available compute resources. On event streams generated by handheld motion in typical scenes, our method achieves throughput above the average event rate, supporting responsive event-based vision on the edge. Extensive benchmarking on standard datasets and live demonstrations confirms the effectiveness of the proposed method. 1