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MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground Imagery
In spite of the many advantages of aerial imagery for crowd monitoring a...
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Iterative Crowd Counting
In this work, we tackle the problem of crowd counting in images. We pres...
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Distribution Matching for Crowd Counting
In crowd counting, each training image contains multiple people, where e...
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Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network
Crowd counting has recently attracted increasing interest in computer vi...
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PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Crowd counting from a single image is a challenging task due to high app...
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Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling
Gatherings of thousands to millions of people occur frequently for an en...
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DENet: A Universal Network for Counting Crowd with Varying Densities and Scales
Counting people or objects with significantly varying scales and densiti...
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W-Net: Reinforced U-Net for Density Map Estimation
Crowd management is of paramount importance when it comes to preventing stampedes and saving lives, especially in a country like China and India where the combined population is a third of the global population. Millions of people convene annually all around the nation to celebrate a myriad of events and crowd count estimation is the linchpin of the crowd management system that could prevent stampedes and save lives. We present a network for crowd counting which reports state of the art results on crowd counting benchmarks. Our contributions are, first, a U-Net inspired model which affords us to report state of the art results. Second, we propose an independent decoding Reinforcement branch which helps the network converge much earlier and also enables the network to estimate density maps with high Structural Similarity Index (SSIM). Third, we discuss the drawbacks of the contemporary architectures and empirically show that even though our architecture achieves state of the art results, the merit may be due to the encoder-decoder pipeline instead. Finally, we report the error analysis which shows that the contemporary line of work is at saturation and leaves certain prominent problems unsolved.
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