The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
Open accessAbstract
We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps. The proposed CSRNet is composed of two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace pooling operations. CSRNet is an easy-trained model because of its pure convolutional structure. We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance. In the ShanghaiTech Part_B dataset, CSRNet achieves 47.3% lower Mean Absolute Error (MAE) than the previous state-of-the-art method. We extend the targeted applications for counting other objects, such as the vehicle in TRANCOS dataset. Results show that CSRNet significantly improves the output quality with 15.4% lower MAE than the previous state-of-the-art approach.
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A review of video surveillance systems
Elharrouss, O., Almaadeed, N., & Al-Maadeed, S. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103116 · Cited by 283
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Metadata
- Authors
- Li, Y., Zhang, X., & Chen, D.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1091-1100
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964209782
- Cited by
- 1,698
- References
- 9
- FWCI
- 49.15
- Type
- conference paper
- Language
- en
- Open access
- green
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