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Person re-identification using deep siamese network with multi-layer similarity constraints

  • 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System
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Abstract

Person Re-identification is aimed to identify a person through multiple camera views. The task has attained a huge research interest due to its apparent importance in surveillance systems from security aspects. This paper introduces a novel methodology based on Siamese architecture with multi-layer similarity constraints. The baseline model embraces two dense blocks to preserve feature maps at each convolutional layer. Besides, the model training is performed by applying distinct similarity constraints on low-level and high-level layers. Two important observations validate the robustness of the proposed model. First, the similarity constraints can synchronize with the model's classification constraints and produce a unified multi-tasking network. Second, the similarity patterns are encoded in the framework in terms of learning parameters during model training. Therefore, a single image is required at the test time instead of the image pair, which makes the method time-efficient and suitable for wide-scale real-time applications. Experimental outcomes on various distinct datasets show that the proposed method surpasses the existing performance benchmarks for person re-identification.

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Authors and Affiliations

  1. DSPM IIIT Naya Raipur, Naya Raipur, India

    Meenakshi Choudhary & Vivek Tiwari

  2. Govt. J. Yoganandam Chhattisgarh College, Raipur, CG, India

    Swati Jain

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  1. Meenakshi Choudhary
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  2. Vivek Tiwari
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  3. Swati Jain
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Correspondence to Swati Jain.

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Choudhary, M., Tiwari, V. & Jain, S. Person re-identification using deep siamese network with multi-layer similarity constraints. Multimed Tools Appl 81, 42099–42115 (2022). https://doi.org/10.1007/s11042-021-11292-2

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