  As an alternative sensing paradigm, dynamic vision sensors (DVS) have been
recently explored to tackle scenarios where conventional sensors result in high
data rate and processing time. This paper 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 IoT 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 usage of simple algorithms like median
filtering and connected component labeling for denoise and region proposal
respectively. To overcome the fragmentation issue, a YOLO inspired neural
network based detector and classifier to merge fragmented region proposals 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 hours of traffic
recording spanning three different locations on two different neuromorphic
sensors (DVS and CeleX) and demonstrate similar performance. Compared to
existing event-based feature trackers, our method provides similar accuracy
while needing approx 6 times 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.
