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.
164 PAPERS • 4 BENCHMARKS
Avenue Dataset contains 16 training and 21 testing video clips. The videos are captured in CUHK campus avenue with 30652 (15328 training, 15324 testing) frames in total.
38 PAPERS • 3 BENCHMARKS
UBnormal is a new supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, the data set introduces abnormal events annotated at the pixel level at training time, for the first time enabling the use of fully-supervised learning methods for abnormal event detection. To preserve the typical open-set formulation, the data set includes disjoint sets of anomaly types in the training and test collections of videos.
31 PAPERS • 3 BENCHMARKS
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".
11 PAPERS • 1 BENCHMARK
The human-Related version of the CUHK Avenue dataset, first presented by Morais et al. in the paper "Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos".
10 PAPERS • 1 BENCHMARK
Unlike previous datasets that focus on detecting the diversity of defect categories (like MVTec AD and VisA), AeBAD is centered on the diversity of domains within the same data category.
8 PAPERS • 2 BENCHMARKS
The Human Related version of UBnormal ("UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection," Acsintoae et al.) was introduced by Flaborea et al. in the paper "Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection".
6 PAPERS • 1 BENCHMARK
CHAD: Charlotte Anomaly Dataset CHAD is high-resolution, multi-camera dataset for surveillance video anomaly detection. It includes bounding box, Re-ID, and pose annotations, as well as frame-level anomaly labels, dividing all frames into two groups of anomalous or normal. You can find the paper with all the details in the following link: CHAD: Charlotte Anomaly Dataset. Please refer to the page of the dataset for more information.
2 PAPERS • NO BENCHMARKS YET
The GoodsAD dataset contains 6124 images with 6 categories of common supermarket goods. Each category contains multiple goods. All images are acquired with 3000 × 3000 high-resolution. The object locations in the images are not aligned. Most objects are in the center of the images and one image only contains a single object. Most anomalies occupy only a small fraction of image pixels. Both image-level and pixel-level annotations are provided.
1 PAPER • NO BENCHMARKS YET
This dataset focuses only on the robbery category, presenting a new weakly labelled dataset that contains 486 new real–world robbery surveillance videos acquired from public sources.
0 PAPER • NO BENCHMARKS YET