* 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](http://pages.ucsd.edu/~ztu/publication/dollarBMVC09ChnFtrs_0.pdf). (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](https://raw.githubusercontent.com/aleju/papers/master/mixed/images/Ten_Years_of_Pedestrian_Detection_What_Have_We_Learned__table.png?raw=true "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)*