The 5th International Competition on Human Identification at a Distance 2024


Welcome to the 5th International Competition on Human Identification at a Distance (HID 2024)!  The competition will be in conjunction with IJCB 2024.

The competition focuses on human identification at a distance (HID) in videos. The dataset proposed for the competition is SUSTech-Competition, a new dataset collected in 2022 and has been used in HID 2023. It contains 859 subjects.


Our sponsor, Watrix Technology, will provide 6 awards (19,000 CNY in total, ~2,660 USD) to the top 6 teams from the second phase.

  • First Prize (1 team): 10,000 CNY (~1,400 USD)
  • Second Prize (2 teams): 3,000 CNY (~420 USD)
  • Third Prize (3 teams): 1,000 CNY (~140 USD)

where CNY stands for Chinese Yuan.

Important Dates

The timeline for the competition is as follows.

  • Competition starts: March 11, 2024
  • Deadline of the 1st phase: May 10, 2024
  • Deadline of the 2nd phase: May 20, 2024
  • Competition results announcement: May 31, 2024
  • Method description submission (only the top 10 teams): June 10, 2024

How to Join the Competition?

The competition is open to everyone. But the members from the teams of the organizers cannot join.

Click CodaLab HID2024 to register and join the competition. 

Note: Please use your institutional email to register for the competition, do NOT use or, otherwise the registration will not be approved.

If you have any questions, please contact Prof. Shiqi Yu<> and CC to Mr. Jingzhe Ma <>.

If you have a WeChat account, you can scan Mr. Jingzhe Ma’s QR code. He can invite you to the WeChat group for this competition. It is optional. The notifications sent through emails will also be announced in that WeChat group.

Competition Sample Code 

You should develop all the source code you need for the competition. You can use OpenGait to help you start your training quickly. There are some well-trained gait recognition models in the OpenGait repo.

The HID 2024 code tutorials for OpenGait can be click here.

How to Achieve a Good Result

The organizers published a summery paper on the previous competition, HID 2023, at International Joint Conference on Biometrics 2023. The paper can be downloaded at here or IEEE.

S. Yu et al., “Human Identification at a Distance: Challenges, Methods and Results on HID 2023,” 2023 IEEE International Joint Conference on Biometrics (IJCB), Ljubljana, Slovenia, 2023, pp. 1-8, doi: 10.1109/IJCB57857.2023.10448952.

  author={Yu, Shiqi and Wang, Chenye and Zhao, Yuwei and Wang, Li and Wang, Ming and Li, Qing and Li, Wenlong and Wang, Runsheng and Huang, Yongzhen and Wang, Liang and Makihara, Yasushi and Ahad, Md Atiqur Rahman},
  booktitle={2023 IEEE International Joint Conference on Biometrics (IJCB)}, 
  title={Human Identification at a Distance: Challenges, Methods and Results on HID 2023}, 

Dataset and Evaluation Protocol

Dataset(New from HID 2023)

Test gallery data set and probe data set download options are provided below:

Since the accuracy in the HID 2022 competition is close to saturation, we switched to a more challenging dataset, SUSTech-Competition, since HID 2023. SUSTech-Competition was collected in the summer of 2022, under the approval of the Southern University of Science and Technology Institutional Review Board. It contains 859 subjects and many variations including clothing, carrying conditions, and view angles have been considered during the data collection. To reduce participants’ burden on data pre-processing, we will provide human body silhouettes. The silhouettes are obtained from the original videos by a deep person detector and a segmentation model provided by our sponsor, Watrix Technology.

All silhouette images will be resized to a fixed size 128 x 128. We will not remove bad-quality silhouettes manually. As shown in the figure, the silhouettes are not of perfect quality. Some noises in real applications are involved. The noises make the competition more challenging. It also makes the competition a good simulation of real applications.

Started from HID 2023, we will not provide a training set to participants. The participants can use any other dataset, such as CASIA-B, OUMVLP, and CASIA-E, to train their algorithms.

The cross-domain challenge should be considered for achieving good results. The gallery set contains only one sequence from each subject. The labels of the sequences in the gallery set will be provided. The probe set contains 5 randomly selected sequences from each subject. In the first phase, only 10% of the probe set will be released to participants to avoid label hacking. The remaining 90% will be released in the second phase.

Evaluation protocol

The evaluation should be user-friendly and convenient for participants. It should also be fair and safe to be hacked. The detailed rules are as follows:

  • To avoid the ID labels of the probe set being hacked by numerous submissions, we will limit the number of submissions each day to 2. Only one CodaLab ID is allowed for a team.
  • The accuracy will be evaluated automatically at CodaLab. The ranking will be updated on the scoreboard accordingly.
  • There will be about 60 days in the first phase. But only 10% of the probe samples will be taken for evaluation in the first phase.
  • There will be only 10 days in the second phase. The remaining 90% of the probe sample is for evaluation. The data is different from that in the first phase.
  • The top 10 teams in the final scoreboard need to send their programs to the organizers. The programs are being run to reproduce their results. The reproduced results should be consistent with the results shown in the CodaLab scoreboard. Otherwise, the results will not be considered.


Privacy and human ethics are our concerns for the competition. Technologies should improve human life and not violate human rights.

The dataset for the competition was collected by the Southern University of Science and Technology, China in 2022. The data collection and usage have been approved by the Southern University of Science and Technology Institutional Review Board (The approval form is attached in the appendix). It contains 859 subjects and was not released to the public in the past. All subjects in the dataset signed agreements to acknowledge that the data could be used for research purposes only. When we release the data during the competition, we will also ask the participants to agree to use the data for research purposes only.

In order to avoid potential privacy issues, we will only provide the binary silhouettes of human bodies, not RGB frames, to participants. The human IDs are labeled as some digital numbers such as 1, 2, 3, … and not their real identification information. Besides, we will also not provide the gender, age, height and other similar information from subjects.

Performance metric

Rank 1 accuracy is for evaluating the methods from different teams. It is straightforward and easy to implement.

where, TP denotes the number of true positives, and N is the number of probe samples.


Advisory Committee (Alphabetical order)

  • Prof. Mark Nixon, University of Southampton, UK
  • Prof. Tieniu Tan, Nanjing University, China; Institute of Automation, Chinese Academy of Sciences, China
  • Prof. Yasushi Yagi, Osaka University, Japan

Organizing Committee (Alphabetical order)

  • Prof. Md. Atiqur Rahman Ahad, University of East London, UK
  • Prof. Yongzhen Huang, Beijing Normal University, China; Watrix Technology Co. Ltd, China
  • Prof. Yasushi Makihara, Osaka University, Japan
  • Prof. Liang Wang, Institute of Automation, Chinese Academy of Sciences, China
  • Prof. Shiqi Yu, Southern University of Science and Technology, China


Q: Can I use data outside of the training set to train my model?
A: Yes, you can. But you must describe what data you use and how to use it in the description of the method.

Q: How many members can my team have?
A: We do not limit the numbers in your team. But only one participant from each team can submit the result, otherwise, it will be considered an invalid result.

Q: Who cannot participate in the competition?
A: The members of the organizers’ research group cannot participate in the competition. The employees and interns at the sponsor company cannot participate in the competition.


Coming Soon