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Counting Crowded Moving Objects
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06), 2006In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be ...
V. Rabaud, S. Belongie
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2017
Most crowd counting methods rely on training with labeled data to learn a mapping between image features and the number of people in the scene. However, the nature of this mapping may change as a function of the scene, camera parameters, illumination etc., limiting the ability of such supervised systems to generalize to novel conditions.
Nada Elassal, James H. Elder
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Most crowd counting methods rely on training with labeled data to learn a mapping between image features and the number of people in the scene. However, the nature of this mapping may change as a function of the scene, camera parameters, illumination etc., limiting the ability of such supervised systems to generalize to novel conditions.
Nada Elassal, James H. Elder
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2020
Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). The stampede incidents frequently occur in large-scale activities at home and abroad, which have caused a lot of casualties.
Peizhi Zeng, Jun Tan
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Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). The stampede incidents frequently occur in large-scale activities at home and abroad, which have caused a lot of casualties.
Peizhi Zeng, Jun Tan
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Attention Scaling for Crowd Counting
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020Convolutional Neural Network (CNN) based methods generally take crowd counting as a regression task by outputting crowd densities. They learn the mapping between image contents and crowd density distributions. Though having achieved promising results, these data-driven counting networks are prone to overestimate or underestimate people counts of ...
Xiaoheng Jiang +7 more
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Auxiliary learning for crowd counting via count-net
Neurocomputing, 2018Abstract This paper aims to develop a simple but effective method that can estimate the number of people in still images. Inspired by the successful applications of deep learning and the appearance of crowd, we design a count-net based on Convolutional Neural Network (CNN).
Youmei Zhang +4 more
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Multidimensional Measure Matching for Crowd Counting
IEEE Transactions on Neural Networks and Learning SystemsThis article addresses the challenge of scale variations in crowd-counting problems from a multidimensional measure-theoretic perspective. We start by formulating crowd counting as a measure-matching problem, based on the assumption that discrete measures can express the scattered ground truth and the predicted density map.
Hui Lin +4 more
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DECCNet: Depth Enhanced Crowd Counting
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019Crowd counting which aims to calculate the number of total instances on an image is a classic but crucial task that supports many applications. Most of the prior works are based on the RGB channels on the images and achieve satisfied performance. However, previous approaches suffer from counting highly congested region due to the incomplete and blurry ...
Shuo-Diao Yang +3 more
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Crowd counting using accumulated HOG
2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016People count is an important indicator in video surveillance. Due to the overlapping objects and cluttered background, counting people accurately in actual crowded scene remains a non-trivial problem. Existing regression-based methods either learn a single model mapping the global feature to people count, or estimate localized count by training a large
Tianchun Xu +3 more
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Confusion Region Mining for Crowd Counting
IEEE Transactions on Neural Networks and Learning SystemsExisting works mainly focus on crowd and ignore the confusion regions which contain extremely similar appearance to crowd in the background, while crowd counting needs to face these two sides at the same time. To address this issue, we propose a novel end-to-end trainable confusion region discriminating and erasing network called CDENet.
Jiawen Zhu +8 more
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A survey of crowd counting and density estimation based on convolutional neural network
Neurocomputing, 2022Zheng Zhang, Guangming Lu, Yu-Dong Zhang
exaly