Mixture of counting cnns: Adaptive integration of cnns specialized to specific appearance for crowd
在人羣統計方面，目前使用一個預測器的方法不能夠很好地處理密度和規模不斷變化的人羣。基於此，作者提出使用多個專門用於特定人羣表面的CNN，這些CNN在多場景圖片訓練中被學出來，並且具有很好地魯棒性。與單CNN和固定權重的聚合CNN相比，此方法具有更低的錯誤率。 上圖是MoC-CNN (Mixture of CountingCNNs)的框架圖，它由兩種類型的CNN構成，專家CNN提取不同人羣表面特徵，
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相關文章
- 1. 人羣計數--Mixture of Counting CNNs
- 2. 論文理解MoC-CNNs
- 3. Pushing the Limits of Deep CNNs for Pedestrian Detection
- 4. 【論文閱讀】Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
- 5. CNNs for Text Classification
- 6. End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human
- 7. 《Architecture of Convolutional Neural Networks (CNNs) demystified》，譯名：卷積神經網絡（CNNs）架構解密
- 8. 人羣分析--Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks
- 9. [論文筆記 CVPR2020]Attention Scaling for Crowd Counting
- 10. CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting