Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EBBIOT: A Low-complexity Tracking Algorithm for Surveillance in IoVT
  Using Stationary Neuromorphic Vision Sensors   In this paper, we present EBBIOT-a novel paradigm for object tracking using
stationary neuromorphic vision sensors in low-power sensor nodes for the
Internet of Video Things (IoVT). Different from fully event based tracking or
fully frame based approaches, we propose a mixed approach where we create
event-based binary images (EBBI) that can use memory efficient noise filtering
algorithms. We exploit the motion triggering aspect of neuromorphic sensors to
generate region proposals based on event density counts with >1000X less memory
and computes compared to frame based approaches. We also propose a simple
overlap based tracker (OT) with prediction based handling of occlusion. Our
overall approach requires 7X less memory and 3X less computations than
conventional noise filtering and event based mean shift (EBMS) tracking.
Finally, we show that our approach results in significantly higher precision
and recall compared to EBMS approach as well as Kalman Filter tracker when
evaluated over 1.1 hours of traffic recordings at two different locations.

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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
2
The categories of this paper are: math
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The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: econ
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci