Survey on Reliable Deep Learning-Based Person Re-Identification Models: Are We There Yet?

• URL: http://arxiv.org/abs/2005.00355v1
• Date: Thu, 30 Apr 2020 16:09:16 GMT
• Title: Survey on Reliable Deep Learning-Based Person Re-Identification Models: Are We There Yet?
• Authors: Bahram Lavi, Ihsan Ullah, Mehdi Fatan, and Anderson Rocha
• Abstract summary: Person re-identification (PReID) is one of the most critical problems in intelligent video-surveillance (IVS) Deep neural networks (DNNs) given their compelling performance on similar vision problems and fast execution at test time. We present descriptions of each model along with their evaluation on a set of benchmark datasets.
• Score: 19.23187114221822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent video-surveillance (IVS) is currently an active research field in computer vision and machine learning and provides useful tools for surveillance operators and forensic video investigators. Person re-identification (PReID) is one of the most critical problems in IVS, and it consists of recognizing whether or not an individual has already been observed over a camera in a network. Solutions to PReID have myriad applications including retrieval of video-sequences showing an individual of interest or even pedestrian tracking over multiple camera views. Different techniques have been proposed to increase the performance of PReID in the literature, and more recently researchers utilized deep neural networks (DNNs) given their compelling performance on similar vision problems and fast execution at test time. Given the importance and wide range of applications of re-identification solutions, our objective herein is to discuss the work carried out in the area and come up with a survey of state-of-the-art DNN models being used for this task. We present descriptions of each model along with their evaluation on a set of benchmark datasets. Finally, we show a detailed comparison among these models, which are followed by some discussions on their limitations that can work as guidelines for future research.

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