# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 5 Discussion of Limitations and Future Outlook  
Since 2016, with the emergence of numerous fall detection methods based on CV technology and the popularity of video surveillance systems, researchers have been considering the application of fall detection technology in PTAs. Falls in crowds are frequently the primary cause of stampede accidents in public spaces. Examples include the stampede accident on the Bund, Shanghai in 2014, the stampede accident on a Mexican subway in 2019, and the trample accident on Itaewon Street, South Korea in 2023. Therefore, accurate and efficient pedestrian fall detection is needed to effectively prevent or reduce stampede accidents in public places. Particularly, crowded public areas are extremely prone to pedestrian falls, as shown in Figure 15.  
Figure 15: Crowd accidents caused by fall behavior. When implementing pedestrian fall detection methods in PTAs, they not only encounter similar challenges as those in the field of elderly medical assistance but also introduce new limitations and challenges. Typical issues include fall detection accuracy, false positives and negatives in fall behavior detection, non-standard posture fall recognition, identifying the location of pedestrian falls, context background of fall behavior, dataset diversity and reliability, scene adaptability, real-time processing performance, hardware compatibility, sensor data reliability, the burden of wearable devices, energy consumption, user personalization capability, user acceptance, and data privacy protection. In the theoretical research stage, ensuring fall detection accuracy is paramount. Not only is high accuracy required, but false recognitions and omissions must also be minimized. In PTAs, the complex site structures, large numbers of diverse and moving pedestrians, potential occlusion phenomena, and various potential fall patterns pose challenges to accurately detect falls. Most existing research focuses on falls occurring while pedestrians are walking or standing, whereas, in real scenarios, pedestrians exhibit complex motion states. Falls can occur in various states such as walking, sitting, squatting, turning, running, etc. After detecting a fall, it is also necessary to identify the exact fall location to notify management personnel appropriately. However, frequently notifying management of every detected fall could burden staff and passenger flow. Therefore, it is necessary to analyze the contextual impact of the fall behavior incident. This includes assessing if the fallen pedestrian has sustained injuries if the fall behavior has disrupted crowd stability and whether it is necessary to dispatch management personnel to the scene. In the experimental verification stage, obtaining a diverse and representative reliable dataset is the primary challenge. The movement characteristics of pedestrians simulating falls in experimental environments differ from those in real scenarios, and significant differences exist between different scenes and pedestrian characteristics. Using standardized datasets with insufficient generalization capabilities can lead to erroneous performance assessments of the proposed models and reduced accuracy in real scenarios. Furthermore, existing research typically validates model effectiveness using pre-collected data. In the actual application process in public transportation, it is necessary to detect pedestrian fall behavior in real time within the scene. PTAs often have numerous video surveillance devices, and the interlinkage mechanisms between multiple cameras and the computational demands of processing large volumes of data pose significant challenges for researchers. In the application configuration stage, hardware compatibility is an unavoidable challenge. When configuring a complete pedestrian fall behavior detection system, theoretical models are constrained by device performance.