IIPA-Net: Joint Illumination-Invariant and Pose-Aligned Feature Learning for Person Reidentification

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Abstract
Person reidentification (re-id) has gained significant progress and aroused great interest in computer vision. However, due to the effect of weak illumination and poor alignment, person re-id is still a challenging task. Many previous works focus on either illumination enhancement methods or pose estimation. However, those methods are difficult to apply in real-world scenarios, which usually contain various interference factors. To improve the performance of re-id, we propose an Illumination-Invariant and Pose-Aligned Network (IIPA-Net). The illumination change is handled by a retinex decompose network, and the pose variation problem is solved by a local feature matching method. Based on the multimodal nature of a person, we propose a part attention module to optimize the global feature. Finally, a data-driven training strategy is proposed to train the proposed architecture effectively. Experiments show that the proposed framework outperforms other state-of-the-art approaches on both normal- and low-light datasets.
Funding Information
  • Science and Technology Program of Shaoguan City of China (200811094530811, 200811094530805, 200811094530423, 2019sn056, SZ2020KJ02, SZ2021KJ05, 2019KTSCX165, 2019KTSCX161, 2022A1515011409, 52105268, 2020kfkt07)
  • Shaoguan University (200811094530811, 200811094530805, 200811094530423, 2019sn056, SZ2020KJ02, SZ2021KJ05, 2019KTSCX165, 2019KTSCX161, 2022A1515011409, 52105268, 2020kfkt07)
  • Key Platforms and Major Scientific Research Projects of Universities in Guangdong (200811094530811, 200811094530805, 200811094530423, 2019sn056, SZ2020KJ02, SZ2021KJ05, 2019KTSCX165, 2019KTSCX161, 2022A1515011409, 52105268, 2020kfkt07)
  • Natural Science Foundation of Guangdong Province (200811094530811, 200811094530805, 200811094530423, 2019sn056, SZ2020KJ02, SZ2021KJ05, 2019KTSCX165, 2019KTSCX161, 2022A1515011409, 52105268, 2020kfkt07)
  • National Natural Science Foundation of China (200811094530811, 200811094530805, 200811094530423, 2019sn056, SZ2020KJ02, SZ2021KJ05, 2019KTSCX165, 2019KTSCX161, 2022A1515011409, 52105268, 2020kfkt07)
  • Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing (200811094530811, 200811094530805, 200811094530423, 2019sn056, SZ2020KJ02, SZ2021KJ05, 2019KTSCX165, 2019KTSCX161, 2022A1515011409, 52105268, 2020kfkt07)

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