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- Publisher
- Springer Journals
- Copyright
- Copyright © Springer-Verlag London Ltd., part of Springer Nature 2019
- Subject
- Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Database Management
- ISSN
- 2192-6611
- eISSN
- 2192-662X
- DOI
- 10.1007/s13735-019-00181-y
- Publisher site
- See Article on Publisher Site
Abstract
Crowd counting is an attracting computer vision problem. Solutions to crowd counting hold high adaptability to other counting problems such as traffic counting and cell counting. Numerous methods have been proposed for the problem. Deep learning-based methods play a significant role in recent advancement. However, no existing literature reviews capture their sophisticated development by challenges. In this paper, we discuss and categorize recent deep learning works in crowd counting by considering how they address the challenges.
Journal
International Journal of Multimedia Information Retrieval – Springer Journals
Published: Jun 30, 2020