Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EBBINNOT: A Hardware Efficient Hybrid Event-Frame Tracker for Stationary
  Dynamic Vision Sensors   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.

18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn