Two-stream Adaptive Convolutional Neural Network for Crowd Counting
Authors: 
Yang Mo, SongLing Fu, QiJun Zhang, Dan Li, Liu Li, ChuanFu XuAuthors Info & Claims
Yang Mo, SongLing Fu, QiJun Zhang, Dan Li, Liu Li, ChuanFu XuAuthors Info & ClaimsPages 152 - 158
Abstract
Due to the high crowd density in some scenes and the large differences in crowd feature scales, it is difficult to count the number of people. The structure of the multi-branch CNN can deal with the features of different scales, improve the accuracy of crowd counting, but also increase the parameters and become more complicated. In this paper, a 2S-ACNN with a simple structure and balanced performance is proposed for crowd counting in a single frame image. The network structure uses different sizes of receptive field to deal with features of different scales, and adds a multi-scale pooling structure to carry out feature fusion, which improve the representation ability of the model. The experimental results show that this method can maintain fewer parameters and achieve lower loss and higher accuracy.
References
[1]
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. 1998. Gradient based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324
[2]
Navneet, D., Triggs, B. 2005. Histogram of oriented gradients for human detection. IN CVPR
[3]
Viola, p., Jones, M. 2004. Robust real-time face detection. International journal of computer vision
[4]
Cortes, Vapnik, V. 1995. Support vector netwos. Machine Learning
[5]
Yoav Freund, Robert E Schapire. 1997. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting[J]. Journal of Computer and System Sciences, 55(1):119-139.
[6]
Girshick, R., Donahue, J., Darrell,T., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR.
[7]
Ross Girshick. 2015. Fast R-CNN. ICCV.
[8]
Ren, S., He, K., Girshick, R., 2017. Faster r-cnn: towards real-time object detection with region proposal networks. Pattern Analysis Mach. Intell., (2017), 39, (6), pp. 1137–1149.
[9]
Redmon, J., Divvala, S., Girshick, R., 2016. You only look once: unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, (November 2016), pp. 779–788.
[10]
Liu, W., Anguelov, D., Erhan, D., 2016. Ssd: single shot multi box detector. Computer Vision ECCV, Amsterdam, Netherlands, (2016), pp. 21–37.
[11]
Nikos Paragios, Visvanathan Ramesh. 2006. A MRF-based Approach for Real-Time Subway Monitoring, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12]
Chen, K., Loy, C., Gong, S., and Xiang, T. 2012. Feature mining for localised crowd counting. In BMVC, volume 1, page 3.
[13]
Chan, A.B., Liang, Z.S.J., and Vasconcelos, N. 2008. Privacy preserving crowd monitoring: Counting people without people models or tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1–7.
[14]
Viet-Quoc Pham, Tatsuo Kozakaya, Osamu Yamaguchi, Ryuzo Okada. 2015. count forest: co-voting uncertain number of targets using random forest for Crowd density estimation, ICCV.
[15]
Cong Zhang, Hongsheng Li, Xiaogang Wang, and Xiaokang Yang. 2015. Cross-scene Crowd Counting via Deep Convolutional Neural Networks, CVPR.
[16]
Yingying Zhang, Desen Zhou, Siqin Chen, 2016. Single-Image Crowd Counting via Multi-Column Convolutional Neural Network, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), pp. 589–597.
[17]
He K, Zhang X, Ren S, and Sun J. 2014. Spatial pyramid pooling in deep convolutional networks for visual recognition. ECCV, 346–361
[18]
Yanghao Li, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang. 2019. Scale-Aware Trident Networks for Object Detection, ICCV.
[19]
Ciregan, D., Meier, U., Schmidhuber, J. 2012. Multi-column deep neural networks for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20]
Kingma, D. P., and Ba, J. 2015. Adam: Amethod for stochastic optimization, ICLR.
Recommendations
Research on Crowd Counting Algorithm Based on Multi-scale Adaptive Network
AIPR '21: Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern RecognitionThe variable spatial scale and crowd distribution in crowd images are the main challenges faced by crowd counting problems in recent years. In order to solve the above problems, a crowd counting method based on a multi-scale adaptive network is proposed ...
Dense crowd counting from still images with convolutional neural networks
We propose a deep learning method for people counting.We provide a new crowd dataset called AHU-CROWD.We test our method on UCSD and UCF-CROWD and compare with the state-of-the-art. For reasons of public security, modeling large crowd distributions for ...
Atrous convolutions spatial pyramid network for crowd counting and density estimation
AbstractScale variation because of perspective distortion is still a challenge for crowd analysis. To address this problem, an atrous convolutions spatial pyramid network (ACSPNet) is proposed to perform crowd counts and density maps for both ...
Comments
Information & Contributors
Information
Published In
December 2020
294 pages
ISBN:9781450388436
DOI:10.1145/3445815
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 March 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CSAI 2020
CSAI 2020: 2020 4th International Conference on Computer Science and Artificial Intelligence
December 11 - 13, 2020
Zhuhai, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 46Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 25 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format