Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting
| Autor: | Usman Sajid, Guanghui Wang, Wenchi Ma |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Mean squared error Computer science Generalization Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Machine Learning (cs.LG) Prior probability 0202 electrical engineering electronic engineering information engineering 0105 earth and related environmental sciences business.industry Perspective (graphical) Process (computing) Pattern recognition (psychology) Metric (mathematics) Benchmark (computing) 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr48806.2021.9412406 |
| Popis: | Crowd counting in still images is a challenging problem in practice due to huge crowd-density variations, large perspective changes, severe occlusion, and variable lighting conditions. The state-of-the-art patch rescaling module (PRM) based approaches prove to be very effective in improving the crowd counting performance. However, the PRM module requires an additional and compromising crowd-density classification process. To address these issues and challenges, the paper proposes a new multi-resolution fusion based end-to-end crowd counting network. It employs three deep-layers based columns/branches, each catering the respective crowd-density scale. These columns regularly fuse (share) the information with each other. The network is divided into three phases with each phase containing one or more columns. Three input priors are introduced to serve as an efficient and effective alternative to the PRM module, without requiring any additional classification operations. Along with the final crowd count regression head, the network also contains three auxiliary crowd estimation regression heads, which are strategically placed at each phase end to boost the overall performance. Comprehensive experiments on three benchmark datasets demonstrate that the proposed approach outperforms all the state-of-the-art models under the RMSE evaluation metric. The proposed approach also has better generalization capability with the best results during the cross-dataset experiments. |
| Databáze: | OpenAIRE |
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