Deep Learning and Multi-Modal Fusion for Real-Time Multi-Object Tracking: Algorithms, Challenges, Datasets, and Comparative Study-Reference-Cited by-同舟云学术

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Deep Learning and Multi-Modal Fusion for Real-Time Multi-Object Tracking: Algorithms, Challenges, Datasets, and Comparative Study

Published:2023 Issue: Volume: Page:
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Author:

Wang Xuan,Sun Zhaojie,Chehri Abdellah,Jeon Gwanggil,Song Yongchao

Publisher

Elsevier BV

Reference143 articles.

1. Point-cloud based 3d object detection and classification methods for self-driving applications: A survey and taxonomy;Duarte Fernandes;Information Fusion,2021

2. A survey of data fusion in smart city applications;Billy Pik;Information Fusion,2019

3. Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects;Isabelle Francisco Luque S�nchez;Information Fusion,2020

4. Object fusion tracking based on visible and infrared images: A comprehensive review;Xingchen Zhang;Information Fusion,2020

5. Exploring fusion strategies for accurate rgbt visual object tracking;Zhangyong Tang;Information Fusion,2023

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