36 datasets found

The WorldExpo’10 dataset contains 199,923 pedestrians labeled at their centers of heads.

The UCF CC 50 dataset contains 50 images collected from publicly available web images.

The ShanghaiTech dataset contains 2 subsets: Part A mainly consists of dense crowd examples and Part B mainly focuses on sparse crowd examples.

The proposed method is conducted on 3 highly challenging publicly datasets: ShanghaiTech, UCF CC 50 and WorldExpo’10.

Multi-modal crowd counting involves estimating crowd density from both visual and thermal/depth images.

Density estimation is one of the most widely used methods for crowd counting in which a deep learning model learns from head-annotated crowd images to estimate crowd density in...

The UCF QNRF dataset is a crowd counting dataset containing 334 images.

The FSC147 dataset is a large-scale dataset for evaluating class-agnostic counting (CAC) models. It contains 6,135 natural images of 147 object categories.

ShanghaiTech Part B is a crowd counting dataset that contains 400 training images and 316 test images.

ShanghaiTech Part A is a crowd counting dataset that contains 300 training images and 182 test images.

The ShanghaiTech B dataset contains images of people in unconstrained crowded scenes.

The ShanghaiTech A dataset contains images of people in unconstrained crowded scenes.

The VisDrone2019 Vehicle dataset contains images of vehicles in unconstrained crowded scenes.

The VisDrone2019 People & Vehicle dataset contains images of people and vehicles in unconstrained crowded scenes.

Crowd counting is to estimate the number of objects in an image of unconstrained congested scenes. Designing a general crowd counting algorithm applicable to a wide range of...

The ShanghaiTech dataset includes 330 training and 107 test videos recorded at 13 different background locations.