TFA-CNN: an efficient method for dealing with crowding and noise problems in crowd counting-Reference-Cited by-同舟云学术

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TFA-CNN: an efficient method for dealing with crowding and noise problems in crowd counting

Published:2023-10-19 Issue:6 Volume:29 Page:3259-3276
ISSN:0942-4962
Container-title:Multimedia Systems
language:en
Short-container-title:Multimedia Systems

Author:

Xiong Liyan,Li Zhida,Huang Xiaohui,Zeng Yijuan,Huang Peng

Funder

National Natural Science Foundation of China

Science and Technology Project of the Transportation Department of Jiangxi Province, China

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s00530-023-01194-8.pdf

Reference63 articles.

1. Wen, L., Du, D., Zhu, P., Hu, Q., Wang, Q., Bo, L., Lyu, S.: Detection, tracking, and counting meets drones in crowds: a benchmark. In: CVPR, pp. 7812–7821 (2021)

2. Liu, N., Long, Y., Zou, C., Niu, Q., Pan, L., Wu, H., ADCrowdNet: An attention-injective deformable convolutional network for crowd understanding. In: CVPR, pp. 3225–3234 (2019)

3. Liang, D., Chen, X., Wei, X., Zhou, Y., Xiang Bai, X., TransCrowd: weakly-supervised crowd counting with transformers. Sci. China Inf. Sci 65(6), 1–14 (2022)

4. Rao, A.S., Gubbi, J., Marusic, S., et al.: Estimation of crowd density by clustering motion cues. Vis. Comput. 31, 1533–1552 (2015)

5. Bo, Wu., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. Int. J. Comput. Vis. 75(2), 247–266 (2007)

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1. CSFNet: A novel counting network based on context features and multi-scale information;2024-07-10

2. MLANet: multi-level attention network with multi-scale feature fusion for crowd counting;Cluster Computing;2024-03-04

3. CSFNet: A novel crowd counting network for occlusion and scale variation;2024-01-22

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