Visual_Tracking_api

This is a simple visual tracking interface coding by Python2.7

Introduction

This repository contains the following contents：

tutorials folder contains the implementation and tutorial of various famous tracking algorithms written with ipython-notebook. Learning these notebooks helps you understand the details of the algorithms.

pyhog folder includes a implementation of HOG feature. We copied this implementation from https://github.com/dimatura/pyhog

python wrapper script file named XXXtracker.py，such as KCFtracker.py. These trackers can be integrated into the VOT evaluation process. There is a demo file vot_demo_tracker.py representing how to write the wrapper script file. You can refer to < Usage > for getting more information.

Trackers

Trackers that have been implemented are as follows:

• KCFtracker.py High-Speed Tracking with Kernelized Correlation Filters (KCF) [PDF]

• DSSTtracker.py Accurate Scale Estimation for Robust Visual Tracking (DSST) [PDF]

• HCFtracker.py Hierarchical Convolutional Features for Visual Tracking (HCF) [PDF]

Environment

Python 2.7.12

scikit-image 0.13.0

scipy 0.19.1

matplotlib 1.5.3

numpy 1.13.1

pytorch 0.1.12

opencv 2.4.11

Usage

Prepare

Before using the tracking interface, you must do some preparation.

First of all, install the necessary library (mentioned in < Environment >)

Then, if the algorithm requires hog features, you must move the folder pyhog to Python site-packages by:

sudo cp -r .../Visual_Tracking_api/pyhog /usr/local/lib/python2.7/site-packages

Or the algorithm requires deep features (we use pretrained vgg19 in general), you need to download model file 'vgg19.pth' by url:

https://download.pytorch.org/models/vgg19-dcbb9e9d.pth

and place it in project directory.

Tracking on you own sequence

You can use this tool to track on you own sequence:

Make sure that your video has been decomposed into image sequences and each image is named with a number (if the current image corresponds to the ith frame in the video, then the name is i.jpg or 0000i.jpg, adding 0 in front of the i is OK). For example:

Except for the image sequence, you need to provide groundtruth.txt file which represents the boundingbox infromation. The boundingbox of the first frame must be give, so there are at least one line in the groundtruth.txt file. For example:

20.0,30.0,50.0,100.0

Represents the boundingbox at (20.0, 30.0) position, width and height are respectively 50.0, 100.0. Of course, if there are other frames of boundingbox information, it can also be written in groundtruth.txt.

We provide tools to run algorithms on custom sequences. example.py is an demo for understanding how to use. First, you need to configure the sequence by

sequence = Sequence(path='/YOUR_ROOT_DIR/YOUR_DATASET_NAME', name='THE_NAME_OF_SEQUENCE', region_format='rectangle')

For example, configure a sequence of vot2016:

sequence = Sequence(path='/media/maoxiaofeng/project/GameProject/dataset/vot2016', name='bag',
region_format='rectangle')

Then, run tracking algorithms on the configured sequence by (If you want to visualize the result, modify visualize to True)

Tracking(sequence,tracker_list=['KCFtracker','DSSTtracker'],visualize = False)

Also you can visulize results directly by ( visualize_gt controls whether to visualize the groundtruth. If tracker_list is None, this function will only visualize the groundtruth. )

visulize_result(sequence,tracker_list=['KCFtracker','DSSTtracker'],visualize_gt = True)

Evaluate on VOT dataset

Now the interface is compatible with VOT dataset, use vot-toolkit to evaluate the tracking algorithm on VOT datasets.

You can download Visual Object Tracking (VOT) challenge datasets through the following links:

VOT2015, VOT2016, VOT2014, VOT2013

Then set up VOT workspace (http://www.votchallenge.net/howto/workspace.html) and integrate trackers into the VOT toolkit (http://www.votchallenge.net/howto/integration.html)

For detail information, please visit VOT official website：http://www.votchallenge.net/