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============================= A few notes on using the dataset ================================ |
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Chen Chen, UNC-Charlotte |
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https://webpages.uncc.edu/cchen62/ |
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chen.chen@uncc.edu |
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Cite our paper: |
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Waqas Sultani, Chen Chen, Mubarak Shah, "Real-world Anomaly Detection in Surveillance Videos" |
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IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 |
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1. Anomaly Detection Experiment |
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- A) Videos: |
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Anomaly-Videos-Part-1 -- Part-4 (4 zip files) |
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Training-Normal-Videos-Part-1.zip and Training-Normal-Videos-Part-2.zip (normal videos for training) |
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Testing_Normal_Videos.zip (normal videos for testing) |
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UCF_Crimes-Train-Test-Split.zip contains the traing and testing split in our experiments |
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(folder: Anomaly_Detection_splits) |
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- B) Temporal Annotations for Testing Videos (anomaly videos) |
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Temporal_Anomaly_Annotation_for_Testing_Videos.txt |
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Each row of 'Temporal_Anomaly_Annotation.txt' is the annotation for a video, for example: |
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Abuse028_x264.mp4 Abuse 165 240 -1 -1 |
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- The first column is the name of the video |
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- The second column is the name of the anomalous event |
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- The third column is the starting frame of the event (you will have to convert each video to image frames first) |
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- The fourth column is the ending frame of the event. |
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- For videos in which second instance of event occurs, fifth and sixth contains starting and ending frames of second instance. |
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Negative number means no anomalous event instance. In this example, abuse (instance) only occurs once. |
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Note: Ours videos have 30 frames per second. |
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2. Anomaly Event Recognition Experiment |
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Classify 13 anomaly events and normal event |
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Normal_Videos_for_Event_Recognition.zip contains the normal videos we used for event recognition experiment |
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Note: rename the unziped folder to "Normal_Videos_event" |
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UCF_Crimes-Train-Test-Split.zip also contains the traing and testing split for event recognition experiment |
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(folder: Action_Regnition_splits) |
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