Downloads

Download our light-weighted, compile-free tracking toolkits or full dataset here.

Toolkit

The benchmark offers light-weighted and compile-free toolkit written in Python. You will find tutorials and examples in the corresponding repositories.

Dataset

To download VideoCube files, please click one of the following links and provide your email address in the new page. We will send you an email with an link to your download.

To make it easier for users to research with VideoCube, we have selected 150 representative sequences from the original version (500 sequences) to form VideoCube-Tiny. The original full version includes 500 sequences (1.4T), while the tiny version includes 150 sequences (344G). Furthermore, we have updated additional semantic information for the VideoCube-Tiny and proposed a new benchmark named MGIT. Please select the suitable version for you.

Full Version (500 Sequences)

Tiny Version and Multi-modal Version (150 Sequences)

DTVLT

Data File Structure

The VideoCube dataset includes 500 sequences, divided into three subsets (train/val/test). The content distribution in each subset still follows the 6D principle proposed in the GIT paper.

The dataset download and file organization process is as follows:

The MGIT dataset includes 150 sequences, divided into three subsets (train/val/test). The content distribution in each subset still follows the 6D principle proposed in the GIT paper.

The dataset download and file organization process is as follows:

Results

Download baseline tracking results and performance reports of 20 public entries on VideoCube from the official account page:

Download baseline tracking results and performance reports of 4 public entries on MGIT from the official account page:

License

The VideoCube and MGIT dataset is licensed under CC BY-NC-SA 4.0. You are free to use the dataset for research purpose. If you want to use it for commercial purpose, please contact us.

Citation

Publication

Global Instance Tracking: Locating Target More Like Humans.
S. Hu, X. Zhao*, L. Huang and K. Huang (*corresponding author)
IEEE Transactions on Pattern Analysis and Machine Intelligence
[DOI] [PDF] [BibTex]

Please cite our IEEE TPAMI paper if VideoCube helps your research.

Publication

A Multi-modal Global Instance Tracking Benchmark (MGIT):
Better Locating Target in Complex Spatio-temporal and Casual Relationship.
S. Hu, D. Zhang, M. Wu, X. Feng, X. Li, X. Zhao and K. Huang
Conference on Neural Information Processing Systems
[PDF] [BibTex]

Please cite our NeurIPS paper if MGIT helps your research.