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Integrated Learning Framework for Pedestrian Tracking

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# Integrated Learning Framework for Pedestrian Tracking

By Taihong Xiao and Jinwen Ma

### Introduction

This repo is an Matlab implementation of our [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-63315-2_9).
ILFPT is in integrated framework designed for pedestrian tracking, especially in the surveillance videos.

Here is a video demo.

[![ILFPT](http://img.youtube.com/vi/HQIi0Z9b4Pw/0.jpg)](https://youtu.be/HQIi0Z9b4Pw?t=0 "Intergrated Framework for Pedestrian Tracking")

### Requirements

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

### Preparation for Testing:

1.  `git clone --recursive https://github.com/Prinsphield/ILFPT.git`
1.  Build Caffe in the `external/caffe`
1.  Download the test videos from either [Google Drive](https://drive.google.com/open?id=0B_ZFgt4zqONCS0lDbmg4NzkxZzQ) or [BaiduPan](https://pan.baidu.com/s/1cvgkJ8) and extract them into the 'test/' directory
1.  Download the detection network model from either [Google Drive](https://drive.google.com/open?id=0B_ZFgt4zqONCaGxfSVJYNXN5X3c) or [BaiduPan](http://pan.baidu.com/s/1mhWzjOs) and extract into `models/` directory
1.  Download our pretrained detection network from either [Google Drive](https://drive.google.com/open?id=0B_ZFgt4zqONCcy12RGF2MVVBQW8) or [BaiduPan](http://pan.baidu.com/s/1gePhQdd) and extract into `output/` directory
1.  Download the pretrained re-id network files from either [Google Drive](https://drive.google.com/open?id=0B_ZFgt4zqONCVUhqc1ZyX21FMWs) or [BaiduPan](http://pan.baidu.com/s/1qYJahB2) and extract into `reid_net/` directory

### Testing Demo

1.  Start Matlab from the root directory
1.  Run `faster_rcnn_build.m`
1.  Run `startup.m`
1.  Run `demo.m`

### Training Your Pedestrian Detection Network

1.  Download SVD-B training data from [OneDrive](https://merced-my.sharepoint.com/:f:/g/personal/txiao3_ucmerced_edu/EsSEdZ7rvTNDufsDxMNKdQQBU0FNoQrvYB8RIGh88S1iSg?e=xAjt2h). Extract them into `dataset/` directory and rename to `VOCdevkit2007/`
1.  Modify related files in `models/` dir to config detection network
1.  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

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