# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 4 International Benchmark Datasets Used for Fall Detection
Examples of these datasets are the Multiple Cameras Fall Dataset (MCFD), Le2i, the High-quality Fall Simulation Dataset (HFSD), and the University of Rzeszow Fall Detection (URFD). MCFD [42]. This dataset contains videos from twenty-four scenes recorded using eight IP cameras. The first twenty-two scenes include various confounding events, including falls, whereas the last two scenes contain confounding events without falls. Confounding  
Figure 8: Examples from the ShanghaiTech Dataset. Figure 10: Examples from the UCF Crime Dataset. Figure 9: Examples from the UMN Dataset. events include activities such as walking, lying on the ground, lying on the sofa, sitting, squatting, and standing. Of the 192 videos, 184 contain fall behaviors. For the data images shown in Figure 11, the frame rate for data collection is 120 fps, and the resolution is \(720\times 480\) pixels. Le2i Fall Detection Dataset [43]. This dataset includes 191 videos of human motion collected from four different environments (\"home\", \"coffee shop\", \"office\", and \"lecture hall\"). The videos range from 30 s to 4 min in length, with an image resolution of \(320\times 240\) pixels. Some examples are shown in Figure 12. Human motion in the videos was performed by different volunteers, and factors such as lighting, clothing color, clothing texture, shadows, reflection, and camera angles were varied during data collection. URFD Dataset [44]. This dataset comprises depth and RGB images collected using two cameras from four scenes. The resolution of the images in Figure 13 is 640 \(\times\) 240 pixels. The dataset contains 40 ADL sequences, 30 fall sequences, and sensor data collected using accelerometers. High-quality Fall Simulation Dataset [45]. This dataset was collected in a nursing home setting using five network cameras. The image resolution is 640 \(\times\) 480 pixels. Ten volunteers performed 55 fall behaviors in different scenarios with variations in fall speed, starting posture, and ending posture. Additionally, data were collected for 17 different ADL scenarios, with an average length of 20 min and 39 s. Examples are shown in Figure 4:0  
SisFall Dataset [46]. Volunteers for this dataset included both young and healthy elderly individuals aged 62 years and above. The young volunteers participated in data collection for 19 ADLs and 15 fall behaviors, whereas the elderly volunteers contributed data for 15 ADLs. The data for each activity were collected using custom devices equipped with two types of accelerometers and a gyroscope. Figure 4:1 Examples from the MCFD. Figure 4:2 Le2i fall detection dataset. Figure 4:3 Examples from the URFD. UP-Fall Detection Dataset [47]. This dataset involved 17 young volunteers aged 18-24 years. The volunteers performed 11 different actions while wearing sensors or in scenarios equipped with environmental and visual sensors. The actions included five fall-related and six daily actions (such as walking, standing, jumping, lying down, and picking up objects), with each action repeated thrice. Mobiact Dataset [48]. This dataset was collected through smartphone sensors carried by volunteers. It is commonly used for smartphone-based pedestrian action recognition research. It involved 50 volunteers participating in nine types of ADLs and 54 volunteers participating in four types of fall behaviors. In summary, all the benchmark datasets were compared with their distinguishing characteristics of scenarios, content, behavior, resolution, and data size. The results are summarized in Figure 4:4  
However, in existing studies on fall detection behavior, many researchers choose to use self-built datasets to validate their proposed methods. These datasets are meticulously curated under controlled conditions, where volunteers are meticulously trained and supervised during the process of data acquisition pertaining to falls and routine activities.