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It’s Not All About Size: On the Role of Data Properties in Pedestrian Detection

  • Amir RasouliEmail author
  • Iuliia Kotseruba
  • John K. Tsotsos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11129)

Abstract

Pedestrian detection is central in applications such as autonomous driving. The performance of algorithms tailored to solve this problem has been extensively evaluated on benchmark datasets, such as Caltech, which do not adequately represent the diversity of traffic scenes. Consequently, the true performance of algorithms and their limitations in practice remain understudied.

To this end, we conduct an empirical study using 7 classical and state-of-the-art algorithms on the recently proposed JAAD dataset augmented with 16 additional labels for pedestrian attributes. Using this data we show that the relative performance of the algorithms varies depending on the properties of the training data.

We analyze the contribution of weather conditions and pedestrian attributes to performance changes and examine the major sources of detection errors. Finally, we show that the diversity of the training data leads to better generalizability of the algorithms across different datasets even with a smaller number of samples.

Keywords

Pedestrian detection Data properties Pedestrian attributes Benchmark dataset Evaluation framework Autonomous driving 

Notes

Acknowledgement

This research was supported by several sources, via grants to the senior author, for which the authors are grateful: Air Force Office of Scientific Research USA (FA9550-18-1-0054), the Canada Research Chairs Program (950-231659), and the Natural Sciences and Engineering Research Council of Canada (RGPIN-2016-05352), and the NSERC Canadian Field Robotics Network (NETGP-417354-11).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer Science and Center for Vision ResearchYork UniversityTorontoCanada

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