Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and
  Sparse Features   We present a pedestrian tracking algorithm, DensePeds, that tracks
individuals in highly dense crowds (greater than 2 pedestrians per square
meter). Our approach is designed for videos captured from front-facing or
elevated cameras. We present a new motion model called Front-RVO (FRVO) for
predicting pedestrian movements in dense situations using collision avoidance
constraints and combine it with state-of-the-art Mask R-CNN to compute sparse
feature vectors that reduce the loss of pedestrian tracks (false negatives). We
evaluate DensePeds on the standard MOT benchmarks as well as a new dense crowd
dataset. In practice, our approach is 4.5 times faster than prior tracking
algorithms on the MOT benchmark and we are state-of-the-art in dense crowd
videos by over 2.6% on the absolute scale on average.

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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: astro-ph
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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph