Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Keypoint-based object tracking and localization using networks of
  low-power embedded smart cameras   Object tracking and localization is a complex task that typically requires
processing power beyond the capabilities of low-power embedded cameras. This
paper presents a new approach to real-time object tracking and localization
using multi-view binary keypoints descriptor. The proposed approach offers a
compromise between processing power, accuracy and networking bandwidth and has
been tested using multiple distributed low-power smart cameras. Additionally,
multiple optimization techniques are presented to improve the performance of
the keypoints descriptor for low-power embedded systems.

25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex