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2015 Fiscal Year Research-status Report

Fast, effective and robust person re-identification for large-scale real applications

Research Project

Project/Area Number 15K16024
Research InstitutionNara Institute of Science and Technology

Principal Investigator

伍 洋  奈良先端科学技術大学院大学, 研究推進機構, 助教 (30750559)

Project Period (FY) 2015-04-01 – 2018-03-31
KeywordsPerson re-identification / Set-based recognition / Deep learning / Metric learning / Efficiency / Scalable / Transfer learning
Outline of Annual Research Achievements

We published 1 international journal paper and 2 international conference papers which are related to this project.
In the international journal paper entitled "Locality based discriminative measure for multiple-shot human re-identification", we proposed a new set-to-set dissimilarity which cares about both majorities and minorities of samples in the set pairs, and explored a local metric field to make the best use of such a dissimilarity for combining locality and metric learning for set-based re-identification. This work is an important component for the whole proposed model.
In the two international conference papers, we investigated hierarchical feature learning and its combination with deep features from convolutional neural networks. This is a new direction that we have explored for borrowing the latest progress from deep learning to enhance our proposed model. The two papers are just some preliminary studies.

Besides that, we have also submitted several other papers (3 journal papers and 1 conference paper) for reviewing, and we are still waiting for their acceptance. Their results will be reported next year.

Current Status of Research Progress
Current Status of Research Progress

3: Progress in research has been slightly delayed.

Reason

We have got encouraging research progress and achievements, and explored new possibilities that we didn't planned to do. However, we weren't able to finished all the planned research for the first year, because:

1. Since the beginning of this project, the principle investigator (PI) has been working at a new position (the actual research staff that takes care of the newly built NAIST International Collaborative Laboratory for Robotics Vision) for establishing the international collaboration and starting several new research topics. The new research topics need more efforts than expected in the beginning, so the work on the project is delayed a little bit.

2. Some new research trend on the research topic appeared and developed very quickly, so the PI spent some time investigating it. More concretely, deep learning models have recently shown striking performances on many recognition problems and also achieved significant better performance than other methods this year on the person re-identification tasks. Therefore, the PI was trying to catch up its latest progress and borrow some key ideas from it for enhancing our research. We also got some initial research outcomes from the study.

Strategy for Future Research Activity

Though the original plan has been delayed a little bit, we still believe that the proposed model is a promising solution. Meanwhile, the latest research progresses can also be adopted to enhance parts of our model. Therefore, the plan for our future work in the left two years will be as follows.
1. To test the state-of-the-art features, including the ones from deep neural networks.
2. To keep implementing the fast clustering of data and fast search techniques.
3. To try some recent metric learning models for a better integration with our collaborative representation model.
4. Doing the experiments not only on our own dataset, but also on a newly published dataset which contains significantly more people.

Causes of Carryover

For the first year, most of the traveling cost has been covered by other budgets of the university, so that more money will be reserved for supporting the publications and presentations of our research results at conferences venues for the next two years.

Expenditure Plan for Carryover Budget

The reserved budget will be used for supporting probably more publications and travelings in the next two years. Meanwhile, we will use some budget for buying a few new PCs to support our research.

  • Research Products

    (4 results)

All 2015 Other

All Int'l Joint Research (1 results) Journal Article (1 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 1 results) Presentation (2 results) (of which Int'l Joint Research: 2 results)

  • [Int'l Joint Research] Xiamen University/Southeast University(China)

    • Country Name
      China
    • Counterpart Institution
      Xiamen University/Southeast University
  • [Journal Article] Locality based discriminative measure for multiple-shot human re-identification2015

    • Author(s)
      Wei Li, Yang Wu, Masayuki Mukunoki, Yinghui Kuang, Michihiko Minoh
    • Journal Title

      Neurocomputing

      Volume: 167 Pages: 280-289

    • DOI

      10.1016/j.neucom.2015.04.068

    • Peer Reviewed / Int'l Joint Research
  • [Presentation] Hierarchical Learning for Large-scale Image Classification via CNN and Maximum Confidence Path2015

    • Author(s)
      Chang Lu, Yanyun Qu, Jianping Fan, Yang Wu, Hanzi Wang
    • Organizer
      The 16th Pacific-Rim Conference on Multimedia
    • Place of Presentation
      Gwangju, Korea
    • Year and Date
      2015-09-16 – 2015-09-18
    • Int'l Joint Research
  • [Presentation] Text Localization with Hierarchical Multiple Feature Learning2015

    • Author(s)
      Yanyun Qu, Li Lin, Weiming Liao, Yang Wu, Hanzi Wang
    • Organizer
      The 16th Pacific-Rim Conference on Multimedia
    • Place of Presentation
      Gwangju, Korea
    • Year and Date
      2015-09-16 – 2015-09-18
    • Int'l Joint Research

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Published: 2017-01-06  

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