CIA

Code for ECCV2022 paper 'Hierarchical Feature Embedding for Visual Tracking', based on PyTorch.

Tracking Libraries

Libraries for implementing and evaluating visual trackers. It includes

• All common tracking and video object segmentation datasets.
• Scripts to analyse tracker performance and obtain standard performance scores.
• General building blocks, including deep networks, optimization, feature extraction and utilities for correlation filter tracking.

Training Framework: LTR

LTR (Learning Tracking Representations) is a general framework for training your visual tracking networks. It is equipped with

• All common training datasets for visual object tracking and segmentation.
• Functions for data sampling, processing etc.
• Network modules for visual tracking.
• And much more...

Trained models

Installation

Clone the GIT repository.

git clone https://github.com/zxgravity/CIA.git

Clone the submodules.

In the repository directory, run the commands:

git submodule update --init  

Install dependencies

Run the installation script to install all the dependencies. You need to provide the conda install path (e.g. ~/anaconda3) and the name for the created conda environment (here pytracking).

bash install.sh conda_install_path pytracking

This script will also download the default networks and set-up the environment.

Note: The install script has been tested on an Ubuntu 16.04 system. In case of issues, check the detailed installation instructions.

Let's test it!

Activate the conda environment and run the script pytracking/run_tracker.py to run CIA18.

conda activate pytracking
cd pytracking
python run_tracker.py CIA CIA18   

Acknowledgments

• Thanks for the project pytracking
• Thanks for the great PreciseRoIPooling module.