3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels

@inproceedings{Zhang20203DCC,
  title={3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels},
  author={Qi Zhang and Antoni B. Chan},
  booktitle={AAAI},
  year={2020}
}
Crowd counting has been studied for decades and a lot of works have achieved good performance, especially the DNNs-based density map estimation methods. Most existing crowd counting works focus on single-view counting, while few works have studied multi-view counting for large and wide scenes, where multiple cameras are used. Recently, an end-to-end multi-view crowd counting method called multi-view multi-scale (MVMS) has been proposed, which fuses multiple camera views using a CNN to predict a… Expand
Cross-View Cross-Scene Multi-View Crowd Counting
  • Qi Zhang, Wei Lin, Antoni B. Chan
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2021
TLDR
A CVCS model that attentively selects and fuses multiple views together using camera layout geometry, and a noise view regularization method to train the model to handle non-correspondence errors is proposed. Expand
CNN-based Density Estimation and Crowd Counting: A Survey
TLDR
Over 220 works are surveyed to comprehensively and systematically study the crowd counting models, mainly CNN-based density map estimation methods to make reasonable inference and prediction for the future development of crowd counting and to provide feasible solutions for the problem of object counting in other fields. Expand
Stacked Homography Transformations for Multi-View Pedestrian Detection
  • Liangchen Song, Jialian Wu, Ming Yang, Qian Zhang, Yuan Li, Junsong Yuan
  • 2021
Multi-view pedestrian detection aims to predict a bird’s eye view (BEV) occupancy map from multiple camera views. This task is confronted with two challenges: how to establish the 3D correspondencesExpand
AutoScale: Learning to Scale for Crowd Counting
TLDR
A simple yet effective Learning to Scale (L2S) module to cope with significant scale variations in both regression and localization and introduces a novel distance label map combined with a customized adapted cross-entropy loss for precise person localization. Expand
CNN-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory Signal
TLDR
This survey is to provide a comprehensive summary of recent advanced crowd counting techniques based on Convolutional Neural Network via density map estimation, and educate new researchers in this field the design principles and trade-offs. Expand
AutoScale: Learning to Scale for Crowd Counting and Localization
  • Chenfeng Xu, Dingkang Liang, +4 authors M. Tomizuka
  • Computer Science
  • 2019
TLDR
A simple and effective Learning to Scale (L2S) module is proposed, which automatically scales dense regions into reasonable closeness levels (reflecting image-plane distance between neighboring people), and also explores the effectiveness of L2S in localizing people by finding the local minima of the quantized distance. Expand
Towards Using Count-level Weak Supervision for Crowd Counting
TLDR
This paper studies the problem of weakly-supervised crowd counting, and devise a simple-yet-effective training strategy, namely Multiple Auxiliary Tasks Training (MATT), to construct regularizes for restricting the freedom of the generated density maps. Expand
Crowd Counting With Partial Annotations in an Image
  • Yanyu Xu, Ziming Zhong, +4 authors Shenghua Gao
  • 2021
To fully leverage the data captured from different scenes with different view angles while reducing the annotation cost, this paper studies a novel crowd counting setting, i.e. only using partialExpand
A Wide Area Multiview Static Crowd Estimation System Using UAV and 3D Training Simulator
TLDR
An automated UAV-based 3D crowd estimation system that can be used for approximately static or slow-moving crowds, such as public events, political rallies, and natural or man-made disasters is proposed. Expand
Accelerated manifold embedding for multi-view semi-supervised classification
TLDR
This paper proposes an auto-weighted manifold embedding model to address multi-view semi-supervised classification problems, where only a small percentage of labeled data points are used for model training. Expand
...
1
2
...

References

SHOWING 1-10 OF 44 REFERENCES
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs
  • Qi Zhang, Antoni B. Chan
  • Computer Science
  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2019
TLDR
A deep neural network framework for multi-view crowd counting, which fuses information from multiple camera views to predict a scene-level density map on the ground-plane of the 3D world is proposed. Expand
Revisiting Perspective Information for Efficient Crowd Counting
TLDR
A perspective-aware convolutional neural network (PACNN) is proposed for efficient crowd counting, which integrates the perspective information into density regression to provide additional knowledge of the person scale change in an image. Expand
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
TLDR
With the proposed simple MCNN model, the method outperforms all existing methods and experiments show that the model, once trained on one dataset, can be readily transferred to a new dataset. Expand
Cross-Camera Knowledge Transfer for Multiview People Counting
TLDR
A novel two-pass framework for counting the number of people in an environment, where multiple cameras provide different views of the subjects, and an algorithm that matches groups of pedestrians in images captured by different cameras is introduced. Expand
Scene invariant multi camera crowd counting
TLDR
The proposed scene invariant crowd counting algorithm enables a pre-trained system to be deployed on a new environment without any additional training, bringing the field one step closer toward a 'plug and play' system. Expand
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
TLDR
A novel approach is proposed that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense crowd image and significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of crowd annotations in the most diverse set of scenes. Expand
Cross-scene crowd counting via deep convolutional neural networks
TLDR
A deep convolutional neural network is proposed for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count, to obtain better local optimum for both objectives. Expand
Towards Perspective-Free Object Counting with Deep Learning
TLDR
A novel convolutional neural network solution, named Counting CNN (CCNN), formulated as a regression model where the network learns how to map the appearance of the image patches to their corresponding object density maps, able to estimate object densities in different very crowded scenarios. Expand
Switching Convolutional Neural Network for Crowd Counting
TLDR
A novel crowd counting model that maps a given crowd scene to its density and switch convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count is proposed. Expand
Multi-source Multi-scale Counting in Extremely Dense Crowd Images
TLDR
This work relies on multiple sources such as low confidence head detections, repetition of texture elements, and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region, and employs a global consistency constraint on counts using Markov Random Field. Expand
...
1
2
3
4
5
...