/video-abnormal-datasets

Datasets for abnormal events

Video datasets

Abnormal Behavior

  • UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection [CVPR 2022]
    • Abnormal events annotated at the pixel level. Abnormal classes: stealing, fighting, smoke & laying down, .etc
    • Project link Paper with code
  • UCF-Crime Dataset [CVPR 2018]
  • CamNuvem: A Robbery Dataset for Video Anomaly Detection [2022]
    • 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.
    • Homepage Paper Dataset Paper with code
  • NWPU Campus dataset [CVPR 2023]
    • NWPU Campus is a dataset proposed for (semi-supervised) video anomaly detection (VAD) and video anomaly anticipation (VAA). It is currently the largest and most complex dataset in its field with 43 scenes, 28 classes of anomalous events and 16 hours of videos. Especially, it contains scene-dependent anomalies, which means an event may be normal in one scene but abnormal in another.
    • Anomalies: playing with water; cycling on footpath; wrong turn. etc
    • Project
  • UBI-Fights [2020]
    • the UBI-Fights dataset is a unique new large-scale dataset of 80 hours of video fully annotated at the frame level. Consisting of 1000 videos, where 216 videos contain a fight event, and 784 are normal daily life situations.
    • Project link Paper with code
  • XD-Violence [2020]
  • HiEve (Challenge and Dataset on Large-scale Human-centric Video Analysis in Complex Events) [2023]
    • we focus on very challenging and realistic tasks of human-centric analysis in various crowd & complex events, including subway getting on/off, collision, fighting, and earthquake escape
    • Project Link Paper with code
  • BEAR: [2023]
    • BEAR is a collection of 18 video datasets grouped into 5 categories (anomaly, gesture, daily, sports, and instructional)
    • Project link
  • A Day on Campus (ADOC) [2020]
  • UCSD Anomaly Detection Dataset [2013]
  • CUHK Avenue Dataset for Abnormal Event Detection [2013]
  • ShanghaiTech [CVPR 2016]
  • Abnormal behavior Data Set
    • Anomalous Behavior Data Set (8 image sequences) Subway Entrance/Exit
    • Project

Traffic accident

Activity

  • CHAD: Charlotte Anomaly Dataset [2023]
    • 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.
    • Project Link
  • UT-interaction [2010]
    • 6 classes of human-human interactions: shake-hands, point, hug, push, kick and punch
    • Project

Image Datasets

  • VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person Detection.

    • Vision-based Fallen Person (VFP290K) is a novel, large-scale dataset for the detection of fallen persons composed of fallen person images collected in various real-world scenarios. VFP290K consists of 294,714 frames of fallen persons extracted from 178 videos, including 131 scenes in 49 locations.
    • Project

  • Street Scene [2020]

    • Street Scene consists of 46 training and 35 testing high resolution 1280×720 video sequences taken from a USB camera overlooking a scene of a two-lane street with bike lanes and pedestrian sidewalks during daytime. There are a total of 56,847 frames for training and 146,410 frames for testing, extracted from the original videos at 15 frames per second. The dataset contains a total of 205 naturally occurring anomalous events ranging from illegal activities such as jaywalking and illegal U-turns to simply those that do not occur in the training set such as pets being walked and a metermaid ticketing a car.
    • Project Paper Paper with Code Github