Ten Years of Pedestrian Detection, What Have We Learned? Ten Years of Pedestrian Detection, What Have We Learned?
Paper summary * 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)*

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