ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

Hussam J. Mohammed (), Shumoos Al-Fahdawi, Alaa S. Al-Waisy, Dilovan Asaad Zebari, Dheyaa Ahmed Ibrahim, Mazin Abed Mohammed, Seifedine Kadry and Jungeun Kim ()
Additional contact information
Hussam J. Mohammed: Computer Center, University of Anbar, Ramadi 31001, Iraq
Shumoos Al-Fahdawi: Computer Science Department, Al-Ma’aref University College, Ramadi 31001, Iraq
Alaa S. Al-Waisy: Computer Engineering Technology Department, Information Technology Collage, Imam Ja’afar Al-Sadiq University, Baghdad 10072, Iraq
Dilovan Asaad Zebari: Department of Computer Science, College of Science, Nawroz University, Duhok 42001, Iraq
Dheyaa Ahmed Ibrahim: Computer Engineering Technology Department, Information Technology Collage, Imam Ja’afar Al-Sadiq University, Baghdad 10072, Iraq
Mazin Abed Mohammed: College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq
Seifedine Kadry: Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
Jungeun Kim: Department of Software, Kongju National University, Cheonan 31080, Korea

Mathematics, 2022, vol. 10, issue 19, 1-22

Abstract: Person re-identification has become an essential application within computer vision due to its ability to match the same person over non-overlapping cameras. However, it is a challenging task because of the broad view of cameras with a large number of pedestrians appearing with various poses. As a result, various approaches of supervised model learning have been utilized to locate and identify a person based on the given input. Nevertheless, several of these approaches perform worse than expected in retrieving the right person in real-time over multiple CCTVs/camera views. This is due to inaccurate segmentation of the person, leading to incorrect classification. This paper proposes an efficient and real-time person re-identification system, named ReID-DeePNet system. It is based on fusing the matching scores generated by two different deep learning models, convolutional neural network and deep belief network, to extract discriminative feature representations from the pedestrian image. Initially, a segmentation procedure was developed based on merging the advantages of the Mask R-CNN and GrabCut algorithm to tackle the adverse effects caused by background clutter. Afterward, the two different deep learning models extracted discriminative feature representations from the pedestrian segmented image, and their matching scores were fused to make the final decision. Several extensive experiments were conducted, using three large-scale and challenging person re-identification datasets: Market-1501, CUHK03, and P-DESTRE. The ReID-DeePNet system achieved new state-of-the-art Rank-1 and mAP values on these three challenging ReID datasets.

Keywords: person re-identification; deep learning; deep belief network; Mask R-CNN; GrabCut algorithm; Market-1501 dataset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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