# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 4 International Benchmark Datasets Used for Fall Detection
An analysis of the literature pertinent to this study reveals that approximately 62.5% of investigations rely on self-built datasets, 25% draw upon publicly accessible datasets, and 12.5% utilize a combination of self-built and public data repositories. The rationale behind this predilection may be attributed to nuanced factors encompassing the contextual fidelity, data precision, and reliability inherent within publicly accessible datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Year** & **Scenario** & **Content** & **Behavior** & **Resolution** & **Size** \\ \hline CUHK [36] & 2013 & Traffic & 37 Video clips & 47 Abnormal Behaviours & 640 \(\times\) 360 & 1.5 GB \\ \hline UCSD [37] & 2013 & Campus & 98 Video clips & 52 Abnormal Behaviours & 240 \(\times\) 360 & 1.5 GB \\ \hline Subway [38] & 2008 & Subways & 2 Video clips & 5 Abnormal Behaviours & 512 \(\times\) 384 & 10 MB \\ \hline SH-Tech [39] & 2016 & Campus & 437 Video clips & Multiple Abnormal Behaviors & 846 \(\times\) 480 & 300 MB \\ \hline UMN [40] & 2009 & 3 scenes & 1 Video clip & Sudden Dispersion & 320 \(\times\) 240 & 250 MB \\ \hline UCF [41] & 2012 & Violent Incidents & 1900 Video clips & Several Violent Behavior & 320 \(\times\) 240 & 90 GB \\ \hline MCFD [42] & 2010 & 24 scenes & 192 Video clips & Fall and Ten Daily Behaviors & 720 \(\times\) 480 & 3.5 GB \\ \hline Le2i [43] & 2013 & 4 scenes & 191 Video clips & \begin{tabular}{c} Fall and Several Daily \\ Behaviors \\ \end{tabular} & 320 \(\times\) 240 & 160 MB \\ \hline URFD [44] & 2014 & 4 scenes & 70 Video clips & \begin{tabular}{c} Fall and Several Daily \\ Behaviors \\ \end{tabular} & 640 \(\times\) 240 & 1.8 GB \\ \hline HQFS [45] & 2016 & Nursing Home & 185 Video clips & \
\begin{tabular}{c} Fall and Several Daily \\ Behaviors \\ \end{tabular} & 640 \(\times\) 480 & 8 GB \\ \hline SiFall [46] & 2017 & Lab Room & Sensor data &
\begin{tabular}{c} 15 Falls and 34 Daily \\ Behaviors \\ \end{tabular} & 600 KB \\ \hline UP-fall [47] & 2019 & 2 scenes & Sensor data & 5 Falls and 6 Daily Behaviors & 1.5 GB \\ \hline Mobiact [48] & 2016 & Lab Room & Sensor data & 4 Falls and 9 Daily Behaviors & 200 KB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: General information of commonly used datasets. Figure 14: Examples from the High-quality Fall Simulation Dataset. The genesis of much early research in fall detection revolved around initiatives targeting eldercare and health assistance, thereby prompting data collection endeavors within enclosed laboratory settings on cushioned surfaces to simulate falls and culminating in the establishment of publicly accessible datasets. However, the perpetuation of utilizing these datasets within a milieu transitioning towards domains, such as public transit, engenders contentious debates. Furthermore, the resolution of video data encapsulated within the majority of datasets remains markedly subpar, substantially trailing the contemporary capabilities of cameras to capture high-definition imagery. Furthermore, [PERSON] et al. [27] pointed out by comparing acceleration features that data collected by volunteers simulating falls in laboratories differs from real pedestrian fall data. For instance, the time interval between the final instant of the free fall phase and the maximum acceleration magnitude in real scenarios is smaller after a fall behavior occurs. This finding prompts researchers to re-examine the effectiveness evaluation strategy of models utilizing synthetically generated datasets based on programmed actions. [PERSON] et al. [34] indicated in their study that the accuracy of fall detection systems can be improved by fusing data from multiple sensors. They suggested that not only different sensor data but also different types of data can be fused. However, current fall detection datasets cannot meet this requirement. In the future, it may be possible to collect pedestrian fall feature data using multiple data collection methods (such as video, sensors, audio, etc.)