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POSNet: a hybrid deep learning model for efficient person re-identification

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Abstract

Person re-identification refers to the process of recognizing a person across several non-overlapping cameras. It is becoming increasingly important in computer vision for real-world surveillance applications. However, the deployment of person-re-identification systems as a surveillance system raises various challenges in their performance. These challenges include limited labeled data, occlusions conditions, human body postures, as well as inter- and intra-class variations. Such challenges deteriorate the effectiveness of person-re-identification systems and lead to the extraction of less discriminative features. Hence, to address these problems, we proposed a hybrid deep learning model, namely POSNet (pseudo-labeled omni-scale network) for efficient person re-identification. The proposed method is referred to as a hybrid because it combines label estimate with modified omni-scale feature learning, i.e., spatiotemporal-assisted omni-scale feature extraction to accomplish person re-identification. To further enhance omni-scale feature learning, we have proposed soft-pool-assisted attention mechanisms during spatial learning. More precisely, soft-pool preserves more important features, and that features are further emphasized by spatial and channel attention layers. Following on, this omni-scale with soft-pool attention learning extracts the spatial information from all frames of videos, and later on, the temporal learning is incorporated using the LSTM model. To handle limited labeled data problems, the proposed hybrid model first assigns pseudo-labels to the unlabeled data and adopts a progressive learning strategy to retrain the model on both labeled and unlabeled data with improved feature extraction, i.e., modified omni-scale feature learning. Moreover, the proposed POSNet model is validated on two large video-based person re-identification datasets, namely MARS and DukeMTMC-Video. It is observed from the research findings that the proposed POSNet outperformed the existing studies with the highest mAP and rank@1 score of 83.7 and 90.3%, respectively.

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The data are publicly available and can also be provided on request to the corresponding author.

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Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1F1A1063134) and also the Chung-Ang University Research Grants in 2022.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

    Eliza Batool, Maryam Bukhari & Muazzam Maqsood

  2. Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, Pakistan

    Saira Gillani

  3. Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan

    Sheneela Naz

  4. Department of Computer Engineering, Mokwon University, Daejeon, 35349, Republic of Korea

    Sang-Soo Yeo

  5. Department of Industrial Security, Chung-Ang University, Seoul, 06974, Republic of Korea

    Seungmin Rho

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  1. Eliza Batool
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  2. Saira Gillani
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  5. Muazzam Maqsood
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Contributions

E.B and S.G finalized the problem formulation; S.G., S.N, and M.M completed the formal analysis; E.B., M.B., and S.G carried out the implementation; E.B, M.B, and S.N finalized the experiments and results; M.B. and M.M finalized figures and typesetting; E.B., M.B, and M.M. wrote the paper; S.R. and S.Y. reviewed and finalized the draft.

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Correspondence to Seungmin Rho.

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Batool, E., Gillani, S., Naz, S. et al. POSNet: a hybrid deep learning model for efficient person re-identification. J Supercomput 79, 13090–13118 (2023). https://doi.org/10.1007/s11227-023-05169-4

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