Abstract

Pedestrian detection is a key problem in computer vision, with several applications including robotics, surveillance and automotive safety. Much of the progress of the past few years has been driven by the availability of challenging public datasets. To continue the rapid rate of innovation, we introduce the Caltech Pedestrian Dataset, which is two orders of magnitude larger than existing datasets. The dataset contains richly annotated video, recorded from a moving vehicle, with challenging images of low resolution and frequently occluded people. We propose improved evaluation metrics, demonstrating that commonly used per-window measures are flawed and can fail to predict performance on full images. We also…

Citation impact

1,338
total citations
FWCI
26.50
Percentile
100%
References
50
Citations per year

Authors

4

Topics & keywords

Keywords
  • Pedestrian detection
  • Benchmark (surveying)
  • Computer science
  • Pedestrian
  • Field (mathematics)
  • Key (lock)
  • Artificial intelligence
  • Robotics
UN Sustainable Development Goals
  • Industry, innovation and infrastructure
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