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Bayesian Loss for Crowd Count Estimation with Point Supervision
In crowd counting datasets, each person is annotated by a point, which i...
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Class-Agnostic Counting
Nearly all existing counting methods are designed for a specific object ...
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People Counting in High Density Crowds from Still Images
We present a method of estimating the number of people in high density c...
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Weighing Counts: Sequential Crowd Counting by Reinforcement Learning
We formulate counting as a sequential decision problem and present a nov...
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Counting people from above: Airborne video based crowd analysis
Crowd monitoring and analysis in mass events are highly important techno...
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W-Net: Reinforced U-Net for Density Map Estimation
Crowd management is of paramount importance when it comes to preventing ...
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Deep Density-aware Count Regressor
We seek to improve crowd counting as we perceive limits of currently pre...
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Distribution Matching for Crowd Counting
In crowd counting, each training image contains multiple people, where each person is annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth each annotated dot or to estimate the likelihood of every pixel given the annotated point. In this paper, we show that imposing Gaussians to annotations hurts generalization performance. Instead, we propose to use Distribution Matching for crowd COUNTing (DM-Count). In DM-Count, we use Optimal Transport (OT) to measure the similarity between the normalized predicted density map and the normalized ground truth density map. To stabilize OT computation, we include a Total Variation loss in our model. We show that the generalization error bound of DM-Count is tighter than that of the Gaussian smoothed methods. In terms of Mean Absolute Error, DM-Count outperforms the previous state-of-the-art methods by a large margin on two large-scale counting datasets, UCF-QNRF and NWPU, and achieves the state-of-the-art results on the ShanghaiTech and UCF-CC50 datasets. Notably, DM-Count ranked first on the leaderboard for the NWPU benchmark, reducing the error of the state-of-the-art published result by approximately 16 https://github.com/cvlab-stonybrook/DM-Count.
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