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Deep learning in video multi-object tracking: A survey
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“Multi-object tracking (MOT) has been extensively explored in recent literature for a wide range of applications [86, 138].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [86]
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Metadata
- Authors
- Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F.
- Affiliations
- University of Salerno (IT)
- Universidad de Granada (ES)
- University of Sannio (IT)
- Corresponding author
- Ciaparrone, Gioele
- ORCID
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 381
- Pages
- 61-88
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2966535964
- Cited by
- 665
- References
- 199
- Citations by year
- 2026: 56 · 2025: 79 · 2024: 97 · 2023: 115 · 2022: 131 · 2021: 128 · 2020: 57
- FWCI
- 35.60
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Neurocomputing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Fundación BBVA
- Agencia Estatal de Investigación
- Ministerio de Ciencia y Tecnología
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- multi-target tracking, video tracking, deep learning, single-camera video, trajectory prediction, object detection
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