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2022 | OriginalPaper | Chapter

Online Multi-object Tracking Based on Deep Learning

Authors : Zheming Sun, Chunjuan Bo, Dong Wang

Published in: Communications, Signal Processing, and Systems

Publisher: Springer Nature Singapore

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Abstract

Multi-object tracking task aims to identify and track all targets in the video. It has important applications in intelligent monitoring and other fields. Two problems can affect the accuracy of the multi-object tracking task. First, occlusion between targets will lead to interruption of tracking trajectory and switch of tracking target. Second, quality of the object detection results will directly affect the tracking accuracy. In this paper, we adopt a single-object tracking algorithm based on deep learning is introduced to solve the first problem and develop a discriminant network scoring the accuracy of detection and prediction bounding boxes to solve the second problem. The experimental results show that the proposed tracker performs better than other competing methods.

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Metadata
Title
Online Multi-object Tracking Based on Deep Learning
Authors
Zheming Sun
Chunjuan Bo
Dong Wang
Copyright Year
2022
Publisher
Springer Nature Singapore
Book
Communications, Signal Processing, and Systems

Print ISBN: 978-981-19-0385-4

Electronic ISBN: 978-981-19-0386-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-19-0386-1_1