Results 231 to 240 of about 124,762 (273)
The construction and optimization of resilient community living sphere driven by digital twins. [PDF]
Sci RepWang L, Zhang X, He W.
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Small Object Localization with 90% Annotation Reduction by Positive-Unlabeled Learning. [PDF]
Micromachines (Basel)Zhou X +6 more
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Deep convolutional neural network-based enhanced crowd density monitoring for intelligent urban planning on smart cities. [PDF]
Sci RepMansouri W +5 more
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Inclusive crowd evacuation modeling under heterogeneous mobility constraints. [PDF]
Sci RepAlqahtani FK +3 more
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MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network. [PDF]
Sensors (Basel)Zhang Y, Song W, Shao M, Liu X.
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Body Structure Aware Deep Crowd Counting
IEEE Transactions on Image Processing, 2018Crowd counting is a challenging task, mainly due to the severe occlusions among dense crowds. This paper aims to take a broader view to address crowd counting from the perspective of semantic modeling. In essence, crowd counting is a task of pedestrian semantic analysis involving three key factors: pedestrians, heads, and their context structure.
Siyu Huang +6 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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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 +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 +4 more
openaire +1 more source
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 +1 more source