Robert0812-midfilter_reid.zip

# midfilter_reid Matlab code for our CVPR 2014 work on learning mid-level filters for person re-identification. Created by [Rui Zhao](www.ee.cuhk.edu.hk/~rzhao), on June 20, 2014. ##Summary In this package, you can find an updated version of MATLAB code for the following paper: - Rui Zhao, Wanli Ouyang, and Xiaogang Wang. Unsupervised Salience Learning for Person Re-identification. In CVPR 2014. ##Install - Download [CUHK01 dataset](https://docs.google.com/forms/d/1MF0gAXWKeO1hpsuHlSpPBS8D5JR-r-QOPtdUoFQJONo/viewform?formkey=dF9pZ1BFZkNiMG1oZUdtTjZPalR0MGc6MA), and put it into directory ./dataset/campus/ - Compile [LibSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/): and put it to directory ./code/libsvm - [GACtoolbox](https://github.com/waynezhanghk/gactoolbox): the gactoolbox in subfolder ./code/gactoolbox is a revised version. Using the original repository may cause problem. - [DenseFeat](https://github.com/Robert0812/dense_feat): we use the dense color and SIFT feature, the code for extracting dense features is preliminarily cloned to ./code/densefeat - PatchMatch: the patch matching code in ./code/patchmatch/rowcolop_core.cpp is clone from Dahua Lin's [Statistical Learning Toolbox](http://www.mathworks.com/matlabcentral/fileexchange/12333-statistical-learning-toolbox), and you need to mex it for using patch match functions. - [Structural SVM](http://www.robots.ox.ac.uk/~vedaldi/code/svm-struct-matlab.html): we only provide a preliminarily compiled lib for 64-bit windows in ./code/rsvm/, for other platforms, you are referred to Prof. Andrea Vedaldi's webpage to download the full code to compile. ##Demos One demo is available: - demo_midfilter_cuhk01.m : perform evaluation on the [CUHK01 dataset](https://docs.google.com/forms/d/1MF0gAXWKeO1hpsuHlSpPBS8D5JR-r-QOPtdUoFQJONo/viewform?formkey=dF9pZ1BFZkNiMG1oZUdtTjZPalR0MGc6MA) ##Remarks - This implementation is a little different than the original version in the training / testing partition, so that the result may vary a little. If you use the default parameter settings, you are suppose to get 33.3% rank-1 matching rate for only one-trial testing. - The training / testing partition is generated following the approach [SDALF](http://www.lorisbazzani.info/code-datasets/sdalf-descriptor/) - This demo was tested on MATLAB (R2012b), 64-bit Windows, Intel Xeon 3.33 GHz CPU - Intermediate cache data would take up to 26GB disk memory - Memory cost of demo on the CUHK01 dataset would be around 40GB. ##Citing our work Please kindly cite our work in your publications if it helps your research: @inproceedings{zhao2014learning, title = {Learning Mid-level Filters for Person Re-identification}, author={Zhao, Rui and Ouyang, Wanli and Wang, Xiaogang}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2014} } ##License Copyright (c) 2014, Rui Zhao All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.