Long-Range Pedestrian Detection using stereo and a cascade of convolutional network classifiers

  • Kira Z
  • Hadsell R
  • Salgian G
 et al. 
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Abstract

In this paper, we present a system for detecting pedestrians at long ranges using a combination of stereo-based detection, classification using deep learning, and a cascade of specialized classifiers that can reduce false positives and computational load. Specifically, we use stereo to perform detection of vertical structures which are further filtered based on edge responses. A convolutional neural network was then designed to support the classification of pedestrians using both appearance and stereo disparity-based features. A second convolutional network classifier was trained specifically for the case of long-range detections using appearance only. We further speed up the classifier using a cascade approach and multi-threading. The system was deployed on two robots, one using a high resolution stereo pair with 180 degree fisheye lenses and the other using 80 degree FOV lenses. Results are demonstrated on a large dataset captured in a variety of environments.

Author-supplied keywords

  • Computer Vision
  • Human detection tracking
  • Recognition

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Authors

  • Zsolt Kira

  • Raia Hadsell

  • Garbis Salgian

  • Supun Samarasekera

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