Behaviour detection in crowded classroom scenes via enhancing features robust to scale and perspective variations-Reference-Cited by-同舟云学术

Behaviour detection in crowded classroom scenes via enhancing features robust to scale and perspective variations

Published:2021-11-09 Issue:14 Volume:15 Page:3466-3475
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Liu Mingyu¹^ORCID,Meng Fanman¹,Wu Qingbo¹,Xu Linfeng¹,Liao Qianghua²

Affiliation:

1. School of Information and Communication Engineering University of Electronic Science and Technology of China West Hi‐Tech Zone Chengdu People's Republic of China

2. Shenzhen Polytechnic Nanshan District Shenzhen China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12318

Reference64 articles.

1. Liu N. et al.:Adcrowdnet: An attention‐injective deformable convolutional network for crowd understanding. In:Proceedings of IEEE/CVF Conference on Computer Vision Pattern Recognition pp.3225–3234.IEEE Piscataway(2019)

2. Nwpu‐crowd: A large‐scale benchmark for crowd counting and localization;Wang Q.;IEEE Trans. Pattern Anal. Mach. Intell.,2020

3. Feature channel enhancement for crowd counting

4. Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset

5. Cai Z. et al.:A unified multi‐scale deep convolutional neural network for fast object detection. In:European Conference on Computer Vision pp.354–370.Springer Berlin(2016)

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