Strong Baseline with Auto-encoder for Scale-Invariant Person Re-identification

Shavantrevva Bilakeri, Karunakar Kotegar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Person Re-identification associates the same person images captured by disjoint cameras. The different training strategies and precise hyper-parameter selection are required for the construction of an effective person re-identification. However, in the current work, the tests are carried out by trial and error, which takes a long time to reach an optimal solution. We experiment with various training strategies with the metric learning model serving as the baseline to address this issue. Existing person re-identification benchmarks have insufficient samples for training the ReID model. Also, the previous approaches fail when the same individual appears in varied sizes. To solve this issue, an auto-encoder module is used to create a single image in three distinct scales to enhance the sample size and tackle the scale variation problem of the person reidentification. In addition, a performance comparison is made between the baseline and the baseline with auto-encoder to demonstrate the influence of an auto-encoder as a data augmentation for the person re-identification. The adoption of auto-encoder module and bestperforming training techniques to a baseline have enhanced the rank1 and mAP on Market1501, DukeMTMC-reID datasets.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages24-29
Number of pages6
ISBN (Electronic)9781665487160
DOIs
Publication statusPublished - 2022
Event6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Shivamogga, India
Duration: 14-10-202215-10-2022

Publication series

Name2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings

Conference

Conference6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022
Country/TerritoryIndia
CityShivamogga
Period14-10-2215-10-22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Control and Optimization

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