Tracking-by-counting: Using network flows on crowd density maps for tracking multiple targets
State-of-the-art multi-object tracking (MOT) methods follow the tracking-by-detection
paradigm, where object trajectories are obtained by associating per-frame outputs of object …
paradigm, where object trajectories are obtained by associating per-frame outputs of object …
Locating and counting heads in crowds with a depth prior
To simultaneously estimate the number of heads and locate heads with bounding boxes, we
resort to detection-based crowd counting by leveraging RGB-D data and design a dual-path …
resort to detection-based crowd counting by leveraging RGB-D data and design a dual-path …
[HTML][HTML] Convolutional-neural network-based image crowd counting: Review, categorization, analysis, and performance evaluation
Traditional handcrafted crowd-counting techniques in an image are currently transformed
via machine-learning and artificial-intelligence techniques into intelligent crowd-counting …
via machine-learning and artificial-intelligence techniques into intelligent crowd-counting …
Coarse-and fine-grained attention network with background-aware loss for crowd density map estimation
In this paper, we present a novel method Coarse-and Fine-grained Attention Network
(CFANet) for generating high-quality crowd density maps and people count estimation by …
(CFANet) for generating high-quality crowd density maps and people count estimation by …
Padnet: Pan-density crowd counting
Crowd counting is a highly challenging problem in computer vision and machine learning.
Most previous methods have focused on consistent density crowds, ie, either a sparse or a …
Most previous methods have focused on consistent density crowds, ie, either a sparse or a …
Hybrid graph neural networks for crowd counting
Crowd counting is an important yet challenging task due to the large scale and density
variation. Recent investigations have shown that distilling rich relations among multi-scale …
variation. Recent investigations have shown that distilling rich relations among multi-scale …
Almost unsupervised learning for dense crowd counting
DB Sam, NN Sajjan, H Maurya, RV Babu - Proceedings of the AAAI …, 2019 - aaai.org
We present an unsupervised learning method for dense crowd count estimation. Marred by
large variability in appearance of people and extreme overlap in crowds, enumerating …
large variability in appearance of people and extreme overlap in crowds, enumerating …
Semi-supervised crowd counting via self-training on surrogate tasks
Most existing crowd counting systems rely on the availability of the object location
annotation which can be expensive to obtain. To reduce the annotation cost, one attractive …
annotation which can be expensive to obtain. To reduce the annotation cost, one attractive …
Learning to count in the crowd from limited labeled data
Recent crowd counting approaches have achieved excellent performance. However, they
are essentially based on fully supervised paradigm and require large number of annotated …
are essentially based on fully supervised paradigm and require large number of annotated …
Exemplar free class agnostic counting
We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object
category at test time without any access to labeled training data for that category. All …
category at test time without any access to labeled training data for that category. All …