An improved event-by-event clustering algorithm for noisy acquisition

Event-based image sensors are a new class of sensors developed thanks to non-uniform sampling and asynchronous technology, which overcomes many image sensor limitations such as a high throughput or a huge power consumption. As their behavior and outputs are really different from traditional image sensors, the produced data stream imposes to completely rethink image processing. Indeed, dedicated algorithms are mandatory to take advantage of this specific data stream, known as Address Event Representation (AER). This paper presents an improved and dedicated event-by-event clustering algorithm allowing the object detection in a noisy environment which is still performant with a SNR of 1/4. We measure high recall and precision for different simulated scenarios with multiple objects and show an improvement compared to the previous algorithm. The approach especially demonstrates a low computational complexity and a reduced memory footprint, which is perfectly suited for low-cost and low-power embedded image sensing applications.