데이터셋 탐색

The Shanghaitech dataset is a large-scale crowd counting dataset. It consists of 1198 annotated crowd images. The dataset is divided into two parts, Part-A containing 482 images and Part-B containing 716 images. Part-A is split into train and test subsets consisting of 300 and 182 images, respectively. Part-B is split into train and test subsets consisting of 400 and 316 images. Each person in a crowd image is annotated with one point close to the center of the head. In total, the dataset consists of 330,165 annotated people. Images from Part-A were collected from the Internet, while images from Part-B were collected on the busy streets of Shanghai. Source: Iterative Crowd Counting Image Source: Li et al

The ShanghaiTech Campus dataset has 13 scenes with complex light conditions and camera angles. It contains 130 abnormal events and over 270, 000 training frames. Moreover, both the frame-level and pixel-level ground truth of abnormal events are annotated in this dataset.

The Fudan-ShanghaiTech dataset (FDST) is a dataset for video crowd counting. It contains 15K frames with about 394K annotated heads captured from 13 different scenes Source: https://arxiv.org/abs/1907.07911

The human-Related version of the ShanghaiTech Campus, was first presented by Morais et al. in the paper "Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos".

To provide ground truth supervision for video consistency modeling, we build up a high-quality dynamic OLAT dataset. Our capture system consists of a light stage setup with 114 LED light sources and Phantom Flex4K-GS camera (global shutter, stationary 4K ultra-high-speed camera at 1000 fps), resulting in dynamic OLAT imageset recording at 25 fps using the overlapping method. Our dynamic OLAT dataset provides sufficient semantic, temporal and lighting consistency supervision to train our neural video portrait relighting scheme, which can generalize to in-the-wild scenarios.