Skip to content

Latest commit

 

History

History
69 lines (59 loc) · 3.91 KB

Ten_Years_of_Pedestrian_Detection_What_Have_We_Learned.md

File metadata and controls

69 lines (59 loc) · 3.91 KB
Raw

Paper

  • Title: Ten Years of Pedestrian Detection, What Have We Learned?
  • Authors: Rodrigo Benenson, Mohamed Omran, Jan Hosang, Bernt Schiele
  • Link: https://arxiv.org/abs/1411.4304
  • Tags: Neural Network, Random Forest, Deformable Parts, SVM, HOG, LVM, pedestrian
  • Year: 2014

Summary

  • What

    • They compare the results of various models for pedestrian detection.
    • The various models were developed over the course of ~10 years (2003-2014).
    • They analyze which factors seemed to improve the results.
    • They derive new models for pedestrian detection from that.

  • Comparison: Datasets

    • Available datasets
      • INRIA: Small dataset. Diverse images.
      • ETH: Video dataset. Stereo images.
      • TUD-Brussels: Video dataset.
      • Daimler: No color channel.
      • Daimler stereo: Stereo images.
      • Caltech-USA: Most often used. Large dataset.
      • KITTI: Often used. Large dataset. Stereo images.
    • All datasets except KITTI are part of the "unified evaluation toolbox" that allows authors to easily test on all of these datasets.
    • The evaluation started initially with per-window (FPPW) and later changed to per-image (FPPI), because per-window skewed the results.
    • Common evaluation metrics:
      • MR: Log-average miss-rate (lower is better)
      • AUC: Area under the precision-recall curve (higher is better)

  • Comparison: Methods

    • Families
      • They identified three families of methods: Deformable Parts Models, Deep Neural Networks, Decision Forests.
      • Decision Forests was the most popular family.
      • No specific family seemed to perform better than other families.
      • There was no evidence that non-linearity in kernels was needed (given sophisticated features).
    • Additional data
      • Adding (coarse) optical flow data to each image seemed to consistently improve results.
      • There was some indication that adding stereo data to each image improves the results.
    • Context
      • For sliding window detectors, adding context from around the window seemed to improve the results.
      • E.g. context can indicate whether there were detections next to the window as people tend to walk in groups.
    • Deformable parts
      • They saw no evidence that deformable part models outperformed other models.
    • Multi-Scale models
      • Training separate models for each sliding window scale seemed to improve results slightly.
    • Deep architectures
      • They saw no evidence that deep neural networks outperformed other models. (Note: Paper is from 2014, might have changed already?)
    • Features
      • Best performance was usually achieved with simple HOG+LUV features, i.e. by converting each window into:
        • 6 channels of gradient orientations
        • 1 channel of gradient magnitude
        • 3 channels of LUV color space
      • Some models use significantly more channels for gradient orientations, but there was no evidence that this was necessary to achieve good accuracy.
      • However, using more different features (and more sophisticated ones) seemed to improve results.

  • Their new model:

    • They choose Decisions Forests as their model framework (2048 level-2 trees, i.e. 3 thresholds per tree).
    • They use features from the Integral Channels Features framework. (Basically just a mixture of common/simple features per window.)
    • They add optical flow as a feature.
    • They add context around the window as a feature. (A second detector that detects windows containing two persons.)
    • Their model significantly improves upon the state of the art (from 34 to 22% MR on Caltech dataset).

Table

Overview of models developed over the years, starting with Viola Jones (VJ) and ending with their suggested model (Katamari-v1). (DF = Decision Forest, DPM = Deformable Parts Model, DN = Deep Neural Network; I = Inria Dataset, C = Caltech Dataset)