A general python framework for visual object tracking and video object segmentation, based on PyTorch.
- Transforming Model Prediction for Tracking | Code available!
- Learning Target Candidate Association to Keep Track of What Not to Track | Code available!
- Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos | Code coming here soon...
Official implementation of the KeepTrack (ICCV 2021), DiMP (ICCV 2019), including complete training code and trained models.
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.
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...
The tracker models trained using PyTracking, along with their results on standard tracking benchmarks are provided in the model zoo.
The toolkit contains the implementation of the following trackers.
[Paper] [Raw results] [Models] [Training Code] [Tracker Code]
Official implementation of KeepTrack. KeepTrack actively handles distractor objects to continue tracking the target. It employs a learned target candidate association network, that allows to propagate the identities of all target candidates from frame-to-frame. To tackle the problem of lacking groundtruth correspondences between distractor objects in visual tracking, it uses a training strategy that combines partial annotations with self-supervision.
[Paper] [Raw results] [Models] [Training Code] [Tracker Code]
Official implementation of the DiMP tracker. DiMP is an end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction. It is based on a target model prediction network, which is derived from a discriminative learning loss by applying an iterative optimization procedure. The model prediction network employs a steepest descent based methodology that computes an optimal step length in each iteration to provide fast convergence. The model predictor also includes an initializer network that efficiently provides an initial estimate of the model weights.
git clone https://github.com/visionml/pytracking.gitIn the repository directory, run the commands:
git submodule update --init 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 pytrackingThis script will also download the default networks and set-up the environment.
Note: The install script has been tested on an Ubuntu 18.04 system. In case of issues, check the detailed installation instructions.
Windows: (NOT Recommended!) Check these installation instructions.
Activate the conda environment and run the script pytracking/run_webcam.py to run ATOM using the webcam input.
conda activate pytracking
cd pytracking
python run_webcam.py keep_track default