By Taihong Xiao and Jinwen Ma

This repo is an Matlab implementation of our paper. ILFPT is in integrated framework designed for pedestrian tracking, especially in the surveillance videos.

Here is a video demo.

1. Caffe (see external/caffe/)
2. MATLAB
3. GPU: GTX 980/1080, Tesla K20/K40/K80

1. git clone --recursive https://github.com/Prinsphield/ILFPT.git
2. Build Caffe in the external/caffe
3. Download the test videos from either Google Drive or BaiduPan and extract them into the 'test/' directory
4. Download the detection network model from either Google Drive or BaiduPan and extract into models/ directory
5. Download our pretrained detection network from either Google Drive or BaiduPan and extract into output/ directory
6. Download the pretrained re-id network files from either Google Drive or BaiduPan and extract into reid_net/ directory

1. Start Matlab from the root directory
2. Run faster_rcnn_build.m
3. Run startup.m
4. Run demo.m

1. Download SVD-B training data from OneDrive. Extract them into dataset/ directory and rename to VOCdevkit2007/
2. Modify related files in models/ dir to config detection network
3. Run scripts in experiments/ accordingly to train a detection network.

Note that the GPU cost for training a detection network is much higher than that for testing. Before training your own detection network, please ensure that your GPU memory memory meets the following requirement:

• 3GB GPU memory for ZF net
• 9GB GPU memory for VGG-16 net