Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review
Author:
Fei Lunlin12, Han Bing3ORCID
Affiliation:
1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China 2. Jiangxi Provincial Transportation Investment Group Co., Ltd., Nanchang 330029, China 3. School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
Abstract
Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.
Funder
Science and Technology Project of Jiangxi Provincial Department of Transport
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference151 articles.
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