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Pixel-wise Crowd Understanding via Synthetic Data
Crowd analysis via computer vision techniques is an important topic in t...
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A-cCCNN: adaptive ccnn for density estimation and crowd counting
Crowd counting, for estimating the number of people in a crowd using vis...
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Optical Flow Dataset and Benchmark for Visual Crowd Analysis
The performance of optical flow algorithms greatly depends on the specif...
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PSCNet: Pyramidal Scale and Global Context Guided Network for Crowd Counting
Crowd counting, which towards to accurately count the number of the obje...
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A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
Estimating count and density maps from crowd images has a wide range of ...
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Plug-and-Play Rescaling Based Crowd Counting in Static Images
Crowd counting is a challenging problem especially in the presence of hu...
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A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View
Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks. The variability of the shooting location adds some intractable challenges to these missions, such as varying scale, unstable exposure, and scene migration. In this paper, we strive to tackle the above challenges and automatically understand the crowd from the visual data collected from drones. First, to alleviate the background noise generated in cross-scene testing, a double-stream crowd counting model is proposed, which extracts optical flow and frame difference information as an additional branch. Besides, to improve the model's generalization ability at different scales and time, we randomly combine a variety of data transformation methods to simulate some unseen environments. To tackle the crowd density estimation problem under extreme dark environments, we introduce synthetic data generated by game Grand Theft Auto V(GTAV). Experiment results show the effectiveness of the virtual data. Our method wins the challenge with a mean absolute error (MAE) of 12.70. Moreover, a comprehensive ablation study is conducted to explore each component's contribution.
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