Journal
2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV)
Volume -, Issue -, Pages 1280-1288Publisher
IEEE
DOI: 10.1109/WACV.2019.00141
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Funding
- NSERC Engage grant
- Sightline Innovation
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In this paper, we consider the problem of crowd counting in images. Given an image of a crowded scene, our goal is to estimate the density map of this image, where each pixel value in the density map corresponds to the crowd density at the corresponding location in the image. Given the estimated density map, the final crowd count can be obtained by summing over all values in the density map. One challenge of crowd counting is the scale variation in images. In this work, we propose a novel scale-aware attention network to address this challenge. Using the attention mechanism popular in recent deep learning architectures, our model can automatically focus on certain global and local scales appropriate for the image. By combining these global and local scale attentions, our model outperforms other state-of-the-art methods for crowd counting on several benchmark datasets.
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