# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 4 International Benchmark Datasets Used for Fall Detection  
The widespread adoption of security surveillance video systems provides sufficient data for related studies. Numerous datasets containing pedestrian data have been used as experimental and validation data for research in this field. The international benchmark abnormal and fall behavior datasets available until now are shown in Figure 4.  
In the early stages of research, datasets that included fall behavior, such as the Chinese University of Hong Kong (CUHK) dataset, the UCSD Anomaly Detection Dataset (UCSD), and the ShanghaiTech Campus dataset, were commonly used. CUHK Avenue Dataset [36]. This dataset was collected from the campus of the Chinese University of Hong Kong. It includes 16 training and 21 testing video segments comprising  
Figure 4: Benchmark Abnormal and Fall Behaviour Datasets. 47 different abnormal events. The data images shown in Figure 5 have a resolution of 640 \(\times\) 360 pixels. UCSD Anomaly Detection Dataset [37]. Data were collected using fixed cameras installed at elevated positions on pedestrian walkways on campus. This dataset defines two categories of anomalies: the presence of nonhuman objects (e.g., vehicles) on walkways and abnormal pedestrian movement patterns (e.g., running and skating). The dataset is divided into two subsets based on different scenes: 50 training and 48 testing segments, with 52 different abnormal behaviors. The data images shown in Figure 6 have resolutions of 240 \(\times\) 360 and 158 \(\times\) 238 pixels, respectively. Subway Dataset [38]. This dataset was collected in two indoor scenarios: subway entrances and exits. It includes two long videos collected in these two scenes, with primarily observed abnormal behaviors, such as walking in the wrong direction, fare evasion, loitering, cleaning activities, crowding, and jumping. The frame images captured from videos have a resolution of 512 \(\times\) 384 pixels shown in Figure 7.  
ShanghaiTech Dataset [39]. The data were collected from 13 scenes, including 330 normal training and 107 abnormal testing videos. They include 13 abnormal behaviors, such as cycling, skateboarding, and fighting. The data images shown in Figure 8 have a resolution of 846 \(\times\) 480 pixels. Figure 5: Examples from the CUHK Avenue Dataset. Note, the red rectangles are the detected boundary of the moving persons. Figure 6: Examples from the UCSD Anomaly Detection Dataset. Note, the red rectangles are the detected boundary of the moving objects. Figure 7: Examples from the Subway Dataset. University of Minnesota (UMN) Dataset [40]. Researchers from the University of Minnesota collected this dataset for three different scenarios: lawns, indoor halls, and squares. It includes 11 short video segments with 7740 frames, and the data images have a resolution of 320 \(\times\) 240 pixels. As Figure 9 shows, normal behavior in the videos involves crowds walking in an orderly fashion, whereas abnormal behavior primarily involves the sudden dispersal of crowds. UCF Crime Dataset [41]. This dataset comprises surveillance videos collected from real-world surveillance cameras. It contains 1900 video segments with a total duration of approximately 128 h. As shown in Figure 10, the data images have a resolution of 320 \(\times\) 240 pixels. The dataset includes 13 real-world abnormal situations: abuse, arrest, arson, assault, traffic accidents, burglary, explosions, fights, robberies, shootings, theft, shoplifting, and vandalism. Video data in the above datasets are collected through cameras in typical high-traffic real-world scenarios, such as campuses and subway stations. These datasets generally classify pedestrian behavior into normal and abnormal behavior, with fall behavior being one type of abnormal behavior. When conducting fall detection research, other abnormal behaviors often cause false alarms. With research progress, datasets were also established for fall detection-related studies, which only include fall behaviors and daily activities.