Survey on Deep Learning Techniques for Person Re-Identification Task

TL;DR

This survey reviews deep learning methods for person re-identification, summarizing state-of-the-art models, their evaluations on benchmarks, and discussing limitations to guide future research in intelligent video surveillance.

Contribution

It provides a comprehensive summary and comparison of deep neural network models for person re-identification, highlighting current limitations and future research directions.

Findings

Deep learning models outperform traditional methods in PReID.

Benchmark evaluations show varying performances among models.

Identified limitations suggest areas for future improvement.

Abstract

Intelligent video-surveillance is currently an active research field in computer vision and machine learning techniques. It provides useful tools for surveillance operators and forensic video investigators. Person re-identification (PReID) is one among these tools. It consists of recognizing whether an individual has already been observed over a camera in a network or not. This tool can also be employed in various possible applications such as off-line retrieval of all the video-sequences showing an individual of interest whose image is given a query, and online pedestrian tracking over multiple camera views. To this aim, many techniques have been proposed to increase the performance of PReID. Among the systems, many researchers utilized deep neural networks (DNNs) because of their better performance and fast execution at test time. Our objective is to provide for future researchers the work being done on PReID to date. Therefore, we summarized state-of-the-art DNN models being used for this task. A brief description of each model along with their evaluation on a set of benchmark datasets is given. Finally, a detailed comparison is provided among these models followed by some limitations that can work as guidelines for future research.