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Proceedings of the VLDB Endowment, 2012
In this paper, we address the problem of selectivity estimation in a crowdsourced database. Specifically, we develop several techniques for using workers on a crowdsourcing platform like Amazon's Mechanical Turk to estimate the fraction of items in a dataset (e.g., a collection of photos) that satisfy some property or predicate (e.g., photos of trees).
Adam Marcus 0002 +4 more
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In this paper, we address the problem of selectivity estimation in a crowdsourced database. Specifically, we develop several techniques for using workers on a crowdsourcing platform like Amazon's Mechanical Turk to estimate the fraction of items in a dataset (e.g., a collection of photos) that satisfy some property or predicate (e.g., photos of trees).
Adam Marcus 0002 +4 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Imbalanced data distribution in crowd counting datasets leads to severe under-estimation and over-estimation problems, which has been less investigated in existing works. In this paper, we tackle this challenging problem by proposing a simple but effective locality-based learning paradigm to produce generalizable features by alleviating sample bias ...
Joey Tianyi Zhou +6 more
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Imbalanced data distribution in crowd counting datasets leads to severe under-estimation and over-estimation problems, which has been less investigated in existing works. In this paper, we tackle this challenging problem by proposing a simple but effective locality-based learning paradigm to produce generalizable features by alleviating sample bias ...
Joey Tianyi Zhou +6 more
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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 0001
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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 0001
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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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Domain Adaptation in Crowd Counting
2020 17th Conference on Computer and Robot Vision (CRV), 2020We consider the problem of domain adaptation in crowd counting. Given an input image of a crowd scene, our goal is to estimate the count of people in the image. Previous work in crowd counting usually assumes that training and test images are captured by the same camera.
Mohammad Asiful Hossain +4 more
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Counting the Crowd at a Carnival
2014The focus of this paper is to count the number of people participating in a specific carnival, namely Aalborg Carnival in Denmark, which is believed to be the biggest in Northern Europe. A carnival poses significant challenges from a computer vision viewpoint due to high density, occlusion and non-human objects in the scene.
Jesper B. Pedersen +4 more
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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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Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence, 2019
To estimate the crowd density map and count the crowd from a single image accurately is always a challenging task. With arbitrary perspective and random crowd density, occlusions, appearance variations and perspective distortions may occur. Some of current crowd counting methods are based on image cropping.
Yuqian Zhang +4 more
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To estimate the crowd density map and count the crowd from a single image accurately is always a challenging task. With arbitrary perspective and random crowd density, occlusions, appearance variations and perspective distortions may occur. Some of current crowd counting methods are based on image cropping.
Yuqian Zhang +4 more
openaire +2 more sources
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 ...
Vincent C. Rabaud, Serge J. Belongie
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Crowd Counting with Dilated Inception Convolution
2021 7th International Conference on Computing and Artificial Intelligence, 2021Convolutional neural network (CNN) has been successfully applied to image-based crowd density estimation. However, large computational resources are required in previous CNN-based methods. Therefore, to overcome these drawbacks, this paper proposes a lightweight crowd density map estimation architecture with Dilated Inception Convolution Neural Network
Chen Hua, Kuang Xu, Tong Tong
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