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.
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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".
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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".
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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".
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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.
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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.
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