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An Enhanced Re-Ranking Model for Person Re-Identification

Jayavarthini Chockalingam*, Malathy Chidambaranathan

Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur, 603203, Tamil Nadu, India

* Corresponding Author: Jayavarthini Chockalingam. Email: email

Intelligent Automation & Soft Computing 2022, 33(2), 697-710.


Presently, Person Re-IDentification (PRe-ID) acts as a vital part of real time video surveillance to ensure the rising need for public safety. Resolving the PRe-ID problem includes the process of matching observations of persons among distinct camera views. Earlier models consider PRe-ID as a unique object retrieval issue and determine the retrieval results mainly based on the unidirectional matching among the probe and gallery images. But the accurate matching might not be present in the top-k ranking results owing to the appearance modifications caused by the difference in illumination, pose, viewpoint, and occlusion. For addressing these issues, a new Hyper-parameter Optimized Deep Learning (DL) approach with Expanded Neighborhood Distance Reranking (HPO-DLDN) model is proposed for PRe-ID. The proposed HPO-DLDN involves different processes for PRe-ID, such as feature extraction, similarity measurement, and feature re-ranking. The HPO-DLDN model uses a Adam optimizer with Densely Connected Convolutional Networks (DenseNet169) model as a feature extractor. Additionally, Euclidean distance-based similarity measurement is employed to determine the resemblance between the probe and gallery images. Finally, the HPO-DLDN model incorporated ENDR model to re-rank the outcome of the person-reidentification along with Mahalanobis distance. An extensive experimental analysis is carried out on CUHK01 benchmark dataset and the obtained results verified the effective performance of the HPO-DLDN model in different aspects.


Cite This Article

J. Chockalingam and M. Chidambaranathan, "An enhanced re-ranking model for person re-identification," Intelligent Automation & Soft Computing, vol. 33, no.2, pp. 697–710, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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