Abnormal High Density Crowds

Data Preprocessing:All video footage in this dataset has undergone comprehensive preprocessing techniques, including frame extraction, anomaly identification, cropping of frames to emphasize crowded scenes, and compression to reduce storage space.Overview:This dataset encompasses four distinct scene incidents, each meticulously detailed below: 1. Times Square Chaos:Video Source: Times Square Chaos from Three Angles Viewpoints: Three different perspectives.View_1: Angled shot, abnormal behavior starts at 00:00:12. Dimensions: 1280x720.View_2: Closeup eye-level shot, abnormal behavior starts at 00:00:53. Dimensions: 1280x720.View_3: Angled shot with cropped dimensions (580x720), abnormal behavior starts at 00:01:39.2. Las Vegas Mass Shooting:Video Source: Las Vegas Mass Shooting CCTV Video Training & Testing Frames: Divided into normal and abnormal instances at different angles and time frames.Train: Wide angled shot, normal behavior between 00:11:17 and 00:16:05. Dimensions: 992x468.Test_1: Same angle as training footage, abnormal behavior between 00:16:06 and 00:17:17. Dimensions: 992x468.Test_2, Test_3, Test_4: Varied angles and grayscale for abnormal behavior instances. Dimensions: 1280x720.3. Love Parade Disaster:Video Source: Love Parade Disaster Training & Testing Frames: Split into normal and abnormal sequences at a specific angle and time frame.Train_1, Train_2: Angled shots with normal behavior within defined time frames. Dimensions: 1280x720.Test: Abnormal behavior between 00:10:48 and 00:11:03. Dimensions: 1280x720. Frame-level labeling stored in XML format.4. Juventus Fan Panic in Italy:Video Sources: Video 1, Video 2 Viewpoints: Divided into two distinct viewpoints comprising abnormal behaviour instances.View_1: Wide, close, eye-level shot with abnormal behavior between 00:00:00 and 00:00:28. Dimensions: 1280x720.View_2: Wide angled shot, abnormal behavior between 00:00:00 and 00:00:28. Dimensions: 880x720.This dataset provides a comprehensive collection of annotated abnormal behaviors captured in different real-life scenarios, crucial for anomaly detection research and analysis.