There have been many methods that have proposed various methods for Crowd Counting. In this paper we propose an application for Real-time Crowd Counting with a selective detection feature and we are using YOLOv4 as a base for object detection and we also a created an analytics system that ...
Crowd Counting Via Scale-adaptive Convolutional Nerual Networkhttps://arxiv.org/abs/1711.04433v2Code:https://github.com/miao0913/SaCNN-CrowdCounting-Tencent_Youtu 为了解决人群密度估计中的 scale and perspective 问题,先前研究者提出使用 多尺度卷积网络来解决多尺度问题 Multiple columns have different receptiv...
Abrupt and continuous nature of scale variation in a crowded scene is a challenging task to enhance crowd counting accuracy in an image. Existing crowd counting techniques generally used multi-column or single-column dilated convolution to tackle scale variation due to perspective distortion. However,...
[YOLO-CROWD] a lightweight crowd counting and face detection model that is based on [YOLO-FaceV2] Technical blog [Chinese Blog] 人群计数论文解读 [Link] [2019.05] [Chinese Blog] C^3 Framework系列之一:一个基于PyTorch的开源人群计数框架 [Link] [2019.04] Crowd counting from scratch [Link] [...
For the near field of vision area, it used the YOLO based network for pedestrian detection and added scene constraints to avoid repeated counting in the near and far field of vision. For the far field of vision area, it used the improved MobileNets to extract the population density ...
A collection of deep learning based RGB-T-Fusion methods, codes, and datasets. The main directions involved are Multispectral Pedestrian Detection, RGB-T Aerial Object Detection, RGB-T Semantic Segmentation, RGB-T Crowd Counting, RGB-T Fusion Tracking. -
The former solution obtains the counting results with the help of object detection networks such as You Only Look Once v4 (YOLOv4) [1] and Single Shot Multibox Detector (SSD) [2], which shows considerable performance under the circumstance with a sparse crowd in sight. However, it may ...
By using a 2D CNN model learn appearance features then represent it as a cuboid. In the cuboid three temporal filters are identified. Then a classifier is applied on concatenated feature vector extracted from cuboid. Crowd counting and crowd density estimation is treated as a regression problem. ...
A collection of RGB-T-Feature-Fusion methods (deep learning methods mainly), codes, and datasets. The main directions involved are Multispectral Pedestrian, RGB-T Vehicle Detection, RGB-T Crowd Counting, RGB-T Fusion Tracking. - datu0615/Awesome-RGBT-Fea
融合CrowdNet算法,其人多时候误差相比YOLOV3要小。 分段加权处理:人数少的时候,YOLO计算结果为主; 人数中等时候,两者加权; 人数较多时候,以CrowdNet计算结果为主 划分训练集和验证集(比例9:1)后,发现训练集中的图片数量不多(2000),因此做了一下简单的数据增强,训练集中的离线数据文件数量增加1倍(=3600),验证集...