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Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method
[article]
2019
arXiv
pre-print
In this work, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only
arXiv:1910.12384v1
fatcat:zxvhjgbanzh3lbfk4uba5nwklu