The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Open accessAbstract
Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes, and severe congestion. Many methods only focus on the local appearance features and they cannot handle the aforementioned challenges. In order to tackle them, we propose a perspective crowd counting network (PCC Net), which consists of three parts: 1) density map estimation (DME) focuses on learning very local features of density map estimation; 2) random high-level density classification (R-HDC) extracts global features to predict the coarse density labels of random patches in images; and 3) fore-/background segmentation (FBS) encodes mid-level features to segments the foreground and background. Besides, the Down, Up, Left, and Right (DULR) module is embedded in PCC Net to encode the perspective changes on four directions (DULR). The proposed PCC Net is verified on five mainstream datasets, which achieves the state-of-the-art performance on the one and attains the competitive results on the other four datasets. The source code is available at https://github.com/gjy3035/PCC-Net.
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Cites
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Zhang, Y., Zhou, D., Chen, S., Gao, S., & Ma, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.70 · Cited by 2,241
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
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Spatial as Deep: Spatial CNN for Traffic Scene Understanding
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Cited by
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
Wang, Q., Gao, J., Lin, W., & Li, X. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3013269 · Cited by 510
Self-citationTransCrowd: weakly-supervised crowd counting with transformers
Liang, D., Chen, X., Xu, W., Zhou, Y., & Bai, X. (2022) · Science China Information Sciences · doi:10.1007/s11432-021-3445-y · Cited by 268
Real-Time Video Emotion Recognition Based on Reinforcement Learning and Domain Knowledge
Zhang, K., Li, Y., Wang, J., Cambria, E., & Li, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3072412 · Cited by 184
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Metadata
- Authors
- Gao, J., Wang, Q., & Li, X.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2020-10
- Volume
- 30
- Issue
- 10
- Pages
- 3486-3498
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2945574898
- Cited by
- 284
- References
- 37
- FWCI
- 16.31
- Type
- journal article
- Language
- en
- Open access
- green
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