文件列表:

compile_matconvnet.m (258, 2016-07-12)
LICENSE (2514, 2016-07-12)
matconvnet\COPYING (735, 2016-07-12)
matconvnet\Makefile (7759, 2016-07-12)
matconvnet\Makefile.mex (793, 2016-07-12)
matconvnet\Makefile.nvcc (925, 2016-07-12)
matconvnet\matlab\src\bits\data.cpp (106, 2016-07-12)
matconvnet\matlab\src\bits\data.cu (9625, 2016-07-12)
matconvnet\matlab\src\bits\data.hpp (4239, 2016-07-12)
matconvnet\matlab\src\bits\datacu.cu (6171, 2016-07-12)
matconvnet\matlab\src\bits\datacu.hpp (2237, 2016-07-12)
matconvnet\matlab\src\bits\datamex.cpp (109, 2016-07-12)
matconvnet\matlab\src\bits\datamex.cu (11354, 2016-07-12)
matconvnet\matlab\src\bits\datamex.hpp (1604, 2016-07-12)
matconvnet\matlab\src\bits\impl\blashelper.hpp (5132, 2016-07-12)
matconvnet\matlab\src\bits\impl\copy.hpp (874, 2016-07-12)
matconvnet\matlab\src\bits\impl\copy_cpu.cpp (595, 2016-07-12)
matconvnet\matlab\src\bits\impl\copy_gpu.cu (625, 2016-07-12)
matconvnet\matlab\src\bits\impl\fast_mutex.h (6940, 2016-07-12)
matconvnet\matlab\src\bits\impl\im2row.hpp (2964, 2016-07-12)
matconvnet\matlab\src\bits\impl\im2row_cpu.cpp (7752, 2016-07-12)
matconvnet\matlab\src\bits\impl\im2row_gpu.cu (10389, 2016-07-12)
matconvnet\matlab\src\bits\impl\imread_gdiplus.cpp (4666, 2016-07-12)
matconvnet\matlab\src\bits\impl\imread_helpers.hpp (14063, 2016-07-12)
matconvnet\matlab\src\bits\impl\imread_libjpeg.cpp (5943, 2016-07-12)
matconvnet\matlab\src\bits\impl\imread_quartz.cpp (5267, 2016-07-12)
matconvnet\matlab\src\bits\impl\nnconv_blas.hpp (12684, 2016-07-12)
matconvnet\matlab\src\bits\impl\nnconv_cudnn.cu (15350, 2016-07-12)
matconvnet\matlab\src\bits\impl\nnconv_cudnn.hpp (2314, 2016-07-12)
matconvnet\matlab\src\bits\impl\nnpooling_cudnn.cu (8135, 2016-07-12)
matconvnet\matlab\src\bits\impl\nnpooling_cudnn.hpp (2549, 2016-07-12)
matconvnet\matlab\src\bits\impl\normalize.hpp (2769, 2016-07-12)
matconvnet\matlab\src\bits\impl\normalize_cpu.cpp (12401, 2016-07-12)
matconvnet\matlab\src\bits\impl\normalize_gpu.cu (5132, 2016-07-12)
matconvnet\matlab\src\bits\impl\pooling.hpp (6307, 2016-07-12)
matconvnet\matlab\src\bits\impl\pooling_cpu.cpp (10332, 2016-07-12)
matconvnet\matlab\src\bits\impl\pooling_gpu.cu (12545, 2016-07-12)
... ...

## MDNet: Multi-Domain Convolutional Neural Network Tracker Created by [Hyeonseob Nam](https://kr.linkedin.com/in/hyeonseob-nam/) and [Bohyung Han](http://cvlab.postech.ac.kr/~bhhan/) at POSTECH Project Webpage: http://cvlab.postech.ac.kr/research/mdnet/ ### Introduction MDNet is the state-of-the-art visual tracker based on a CNN trained on a large set of tracking sequences, and the winner tracker of [The VOT2015 Challenge](http://www.votchallenge.net/vot2015/). Detailed description of the system is provided by our [arXiv technical report](http://arxiv.org/abs/1510.07945). This software is implemented using [MatConvNet](http://www.vlfeat.org/matconvnet/) and part of [R-CNN](https://github.com/rbgirshick/rcnn). ### Citation If you're using this code in a publication, please cite our paper. @InProceedings{nam2016mdnet, author = {Nam, Hyeonseob and Han, Bohyung}, title = {Learning Multi-Domain Convolutional Neural Networks for Visual Tracking}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2016} } ### License This software is being made available for research purpose only. Check LICENSE file for details. ### System Requirements This code is tested on *** bit Linux (Ubuntu 14.04 LTS). **Prerequisites** 0. MATLAB (tested with R2014a) 0. MatConvNet (tested with version 1.0-beta10, included in this repository) 0. For GPU support, a GPU (~2GB memory) and CUDA toolkit according to the [MatConvNet installation guideline](http://www.vlfeat.org/matconvnet/install/) will be needed. ### Installation 0. Compile MatConvNet according to the [installation guideline](http://www.vlfeat.org/matconvnet/install/). An example script is provided in 'compile_matconvnet.m'. 0. Run 'setup_mdnet.m' to set the environment for running MDNet. ### Online Tracking using MDNet **Pretrained Models** If you only need to run the tracker, you can use the pretrained MDNet models: 0. models/mdnet_vot-otb.mat (trained on VOT13,14,15 excluding OTB) 0. models/mdnet_otb-vot14.mat (trained on OTB excluding VOT14) 0. models/mdnet_otb-vot15.mat (trained on OTB excluding VOT15) **Demo** 0. Run 'tracking/demo_tracking.m'. The demo performs online tracking on *'Diving'* sequence using a pretrained model 'models/mdnet_vot-otb.mat'. In case of out of GPU memory, decrease *opts.batchSize_test* in 'tracking/mdnet_init.m'. You can also disable the GPU support by setting *opts.useGpu* in 'tracking/mdnet_init.m' to false (not recommended). ### Learning MDNet **Preparing Datasets** You may need OTB and VOT datasets for learning MDNet models. You can also use other datasets by configuring 'utils/genConfig.m'. 0. Download [OTB](http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html) and [VOT](http://www.votchallenge.net/) datasets. 0. Locate the OTB sequences in 'dataset/OTB' and VOT201x sequences in 'dataset/VOT/201x', or modify the variables *benchmarkSeqHome* in 'utils/genConfig.m' properly. **Demo** 0. Run 'pretraining/demo_pretraining.m'. The demo trains new MDNet models using OTB or VOT sequences.

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