EBBINNOT : a hardware-efficient hybrid event-frame tracker for stationary dynamic vision sensors

Abstract—As an alternative sensing paradigm, dynamic vision sensors (DVSs) have been recently explored to tackle scenarios where conventional sensors result in high data rate and processing time. This article presents a hybrid event-frame approach for detecting and tracking objects recorded by a stationary neuromorphic sensor, thereby exploiting the sparse DVS output in a low-power setting for traffic monitoring. Specifically, we propose a hardware-efficient processing pipeline that optimizes memory and computational needs that enable long-term battery-powered usage for Internet of Things applications. To exploit the background removal property of a static DVS, we propose an event-based binary image creation that signals presence or absence of events in a frame duration. This reduces memory requirement and enables the usage of simple algorithms like median filtering and connected component labeling for denoise and region proposal (RP), respectively. To overcome the fragmentation issue, a YOLO-inspired neural network-based detector and classifier to merge fragmented RPs has been proposed. Finally, a new overlap-based tracker was implemented, exploiting overlap between detections and tracks is proposed with heuristics to overcome occlusion. The proposed pipeline is evaluated with more than 5 h of traffic recording spanning three different locations on two different neuromorphic sensors (DVS and CeleX) and demonstrates similar performance. Compared to existing event-based feature trackers, our method provides similar accuracy while needing ≈ 6× less computes. To the best of our knowledge, this is the first time a stationary DVS-based traffic monitoring solution is extensively compared to simultaneously recorded RGB frame-based methods while showing tremendous promise by outperforming state-of-the-art deep learning solutions. The traffic data set is publicly made available at: https://nusneuromorphic.github.io/dataset/index.html.