acfReadme

PURPOSE ^

Aggregate Channel Features Detector Overview.

SYNOPSIS ^

This is a script file.

DESCRIPTION ^

 Aggregate Channel Features Detector Overview.

 Piotr's Computer Vision Matlab Toolbox      Version 3.40
 Copyright 2014 Piotr Dollar.  [pdollar-at-gmail.com]
 Licensed under the Simplified BSD License [see external/bsd.txt]

 %%%%%%%%%%%%%%%%%%%%%%%%%%%% 1. Introduction. %%%%%%%%%%%%%%%%%%%%%%%%%%%

 The detector portion of this toolbox implements the Aggregate Channel
 Features (ACF) object detection code. The ACF detector is a fast and
 effective sliding window detector (30 fps on a single core). It is an
 evolution of the Viola & Jones (VJ) detector but with an ~1000 fold
 decrease in false positives (at the same detection rate). ACF is best
 suited for quasi-rigid object detection (e.g. faces, pedestrians, cars).

 The detection code was written by Piotr Dollár with contributions by Ron
 Appel and Woonhyun Nam (with bug reports/suggestions from many others).

 %%%%%%%%%%%%%%%%%%%%%%%%%%%% 2. Papers. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 The detector was introduced and described through the following papers:
  [1] P. Dollár, Z. Tu, P. Perona and S. Belongie
   "Integral Channel Features", BMVC 2009.
  [2] P. Dollár, S. Belongie and P. Perona
   "The Fastest Pedestrian Detector in the West," BMVC 2010.
  [3] P. Dollár, R. Appel and W. Kienzle
   "Crosstalk Cascades for Frame-Rate Pedestrian Detection," ECCV 2012.
  [4] P. Dollár, R. Appel, S. Belongie and P. Perona
   "Fast Feature Pyramids for Object Detection," PAMI 2014.
  [5] W. Nam, P. Dollár, and J.H. Han
   "Local Decorrelation For Improved Pedestrian Detection," NIPS 2014.
 Please see: http://vision.ucsd.edu/~pdollar/research.html#ObjectDetection

 A short summary of the papers, organized by detector name:

 [1] "Integral Channel Features" [ICF] - Introduced channel features and
 modified the VJ framework to compute integral images (and Haar wavelets)
 over the channels. Substantially outperformed HOG and at faster speeds.

 [2] "Fastest Pedestrian Detector in the West" [FPDW] - We observed that
 features computed at one scale can be used to approximate features at
 nearby scales, increasing detector speed with little loss in accuracy.

 [3] "Crosstalk Cascades" - This work coupled cascade evaluation at nearby
 positions and scales to exploit correlations in detector responses at
 neighboring locations. Further increased speed of the ICF detector.

 [4] "Aggregate Channel Features" [ACF] - We found that single-scale
 square Haar wavelets were sufficient in the ICF framework. Thus instead
 of computing integral images and Haar wavelets, we simply smooth and
 downsample the channels and the features are now single pixel lookups in
 the "aggregated" channels.

 [5] "Locally Decorralated Channel Features" [LDCF] - Filtering the
 channel features with appropriate data-derived filters can remove local
 correlations from the channels. Given decorrelated features, boosted
 decision trees generalize much better giving a nice boost in accuracy.

 This code implements ACF [4] and LDCF [5]. It does not implement ICF [1]
 or FPDW [2] which are now obsolete and supplemented by ACF. Crosstalk
 cascades [3] are also not used as classifier evalution in ACF is very
 fast (no need to compute Haar wavelets). However, ACF does use the simple
 but highly effective "constant soft cascades" from [3].

 Please cite a subset of the above papers as appropriate if you end up
 using this code to support a publication. Thanks!

 %%%%%%%%%%%%%%%%%%%%%%%%%%%% 3. Setup. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 (A) Please install and setup the toolbox as described online:
  http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
 You may need to recompile for your system, see toolboxCompile. Note:
 enabling OpenMP during compile will significantly speed training.

 (B) Important: to train the detectors and run the detection demos you
 need to install the Caltech Pedestrian Detection Benchmark available at:
  http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
 In particular, make sure to download and install:
  (B1) Matlab evaluation/labeling code version 3.2.1 or later
  (B2) INRIA data (necessary for the INRIA demo)
  (B3) Caltech-USA data (necessary for the Caltech demo)
 Please follow the instruction in the readme of the Caltech code. You only
 need to download the data and code and place appropriately, there is no
 need to look closely at the evaluation code. Initially running the demos
 (acfDemoInria and acfDemoCal) will convert the data from the Caltech data
 format to a format useable by ACF. If this step fails it means the
 Caltech code or data is not properly setup.

 %%%%%%%%%%%%%%%%%%%%%%%%%%%% 4. Getting Started. %%%%%%%%%%%%%%%%%%%%%%%%

 After performing the setup, see acfDemoInria.m and acfDemoCal.m for demos
 and visualizations.

 For an overview of available functionality please see detector/Contents.m
 and channels/Contents.m. The various detector/acf*.m and channels/chns*.m
 functions are well documented and worth checking for additional details.

 Finally, a note about pre-trained models. The detector/models/ directory
 contains four pre-trained pedestrian models (ACF/LDCF on INRIA/Caltech).
 Running acfDemoInria/Cal.m with the ACF/LDCF flag toggled gives rise to
 these models (just delete the existing models to retrain from scratch).
 Note, however, that results will differ by up to +/-2% MR depending on
 operating system and random seed (see opts.seed), and the models here are
 not exactly equivalent to the models in the papers (due to evolution of
 the code). Small changes in MR should not be considered significant (nor
 should they be used as a basis for publishing). Whenever making a change
 I suggest training/testing the same model with multiple random seeds.

 Enjoy and I hope you find the detectors useful :)

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