## 5 Experiments  
### Datasets  
The proposed framework was evaluated using three challenging, publicly available anomaly datasets that are commonly used as benchmark datasets; the UCSD-Ped2, CUHK Avenue, and ShanghaiTech, and the extra-large CHAD anomaly dataset. * UCSD-Pedestrian 2 (Ped2) [29] is considered one of the most popular datasets for unsupervised video abnormality detection. Researchers at the University of California, San Diego (UCSD) produced this dataset in 2010 to record usual pedestrian activity and rare anomalies in a controlled setting. The videos were taken from the same area at various times of the day, with varying lighting and shadow effects, which complicate the anomaly identification procedure. In this dataset, pedestrians crossed in front of the camera. Normal circumstances usually involve people strolling along walkways, but abnormalities are distinguished by the appearance of strange objects (like carts, bicycles, skateboards, etc. ), strange motion patterns (like skating on a board), and people walking on grass. Ped2 consists of 16 videos with 2550 frames for training and 12 videos with 2010 frames for testing, each with a size of 240 by 360 pixels;
* The CUHK Avenue [30]: the Chinese University of Hong Kong (CUHK) produced the CUHK Avenue dataset in 2013, which provides a broader variety of anomalies in an open campus setting. The videos are captured from a single scene of the campus avenue. With 21 test films and 16 training videos, there are a total of 15,324 frames for testing and 15,328 frames for training in this dataset. The resolution of each frame is 360 by 640 pixels, and around 47 anomalous behaviors, such as loitering, throwing objects, and running through the gate, were noted. The CUHK Avenue dataset's training and testing clips are no longer than two minutes;
* ShanghaiTech [31]: the most extensive and available unsupervised dataset for video anomaly detection, consisting of 437 clips from 13 cameras positioned across the ShanghaiTech campus at a frame resolution of 856 \(\times\) 480 pixels. It has 107 test videos with both normal and abnormal occurrences; there are a total of 130 abnormal events, and 330 training clips containing 274,515 frames with only normal events. Every scenario in the videos has a different cast of individuals, difficult lighting, and unusual camera angles. Among the human anomalies in the dataset are activities like skateboarding, riding motorcycles and bikes, chasing, fighting, and jogging. All videos have frame and pixel annotations;
* The CHAD [32] is a multi-camera anomaly dataset set in a commercial parking lot, which includes 420 videos with over 1.15 million high-resolution frames from four camera views. Scenes 1-3 and Scene 4 are captured at 30 frames per second, and at 1920 \(\times\) 1080 resolution and 1280 \(\times\) 720 resolution, respectively. According to its authors, it is the largest fully annotated anomaly detection dataset, offering detailed person annotations from continuous video streams captured by stationary cameras. The CHAD provides human detection, tracking, and pose annotations encompassing four types: frame-level anomaly labels, person bounding boxes, person ID labels, and human key points. It includes 59,172 anomalous frames representing 22 distinct behaviors categorized into group and individual activities, and 1,093,477 normal frames. The group activities comprise fighting, punching, pushing, pulling, slapping, strangling, theft, etc., while the individual activities include throwing, running, riding, falling, littering, etc. The summary for the four video anomaly datasets is shown in Table 1. Different samples from the datasets displayed in Figure 5 vary from normal to anomaly, with short descriptions for anomaly samples.