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| <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" | ||
| "http://www.w3.org/TR/REC-html40/loose.dtd"> | ||
| <html> | ||
| <head> | ||
| <title>Description of acfReadme</title> | ||
| <meta name="keywords" content="acfReadme"> | ||
| <meta name="description" content="Aggregate Channel Features Detector Overview."> | ||
| <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1"> | ||
| <meta name="generator" content="m2html © 2003 Guillaume Flandin"> | ||
| <meta name="robots" content="index, follow"> | ||
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| </head> | ||
| <body> | ||
| <a name="_top"></a> | ||
| <!-- menu.html detector --> | ||
| <h1>acfReadme | ||
| </h1> | ||
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| <h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2> | ||
| <div class="box"><strong>Aggregate Channel Features Detector Overview.</strong></div> | ||
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| <h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2> | ||
| <div class="box"><strong>This is a script file. </strong></div> | ||
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| <h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2> | ||
| <div class="fragment"><pre class="comment"> Aggregate Channel Features Detector Overview. | ||
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| 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] | ||
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| %%%%%%%%%%%%%%%%%%%%%%%%%%%% 1. Introduction. %%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
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| 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). | ||
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| The detection code was written by Piotr Dollár with contributions by Ron | ||
| Appel and Woonhyun Nam (with bug reports/suggestions from many others). | ||
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| %%%%%%%%%%%%%%%%%%%%%%%%%%%% 2. Papers. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
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| 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 | ||
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| A short summary of the papers, organized by detector name: | ||
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| [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. | ||
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| [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. | ||
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| [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. | ||
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| [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. | ||
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| [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. | ||
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| 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]. | ||
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| Please cite a subset of the above papers as appropriate if you end up | ||
| using this code to support a publication. Thanks! | ||
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| %%%%%%%%%%%%%%%%%%%%%%%%%%%% 3. Setup. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
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| (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. | ||
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| (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. | ||
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| %%%%%%%%%%%%%%%%%%%%%%%%%%%% 4. Getting Started. %%%%%%%%%%%%%%%%%%%%%%%% | ||
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| After performing the setup, see acfDemoInria.m and acfDemoCal.m for demos | ||
| and visualizations. | ||
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| 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. | ||
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| 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. | ||
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| Enjoy and I hope you find the detectors useful :)</pre></div> | ||
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| <hr><address>Generated by <strong><a href="http://www.artefact.tk/software/matlab/m2html/" target="_parent">m2html</a></strong> © 2003</address> | ||
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