## 4 Experimental Environment and Results  
### The Dataset  
The dataset is obtained from various surveillance footage in the city of Busan Citizen's Park, the Republic of Korea, with the permission of the Electronics and Telecommunication Research Institute (ETRI). Conventionally placed surveillance cameras (on top of metal poles) capture multiple areas. The full-body profile of each person is visible without undesired high-angle views. Furthermore, a ground-truth tool is made to crop and extract the pedestrians from multiple images. All of the images in the dataset have the same external conditions, such as lighting and the time of day. The total number of images in the dataset and the class-specific numbers can be seen in Table 1. The dataset split is arranged for multi-class normality, where a single class in the dataset is chosen to be the anomaly, and the rest of the classes are assigned as the normal class.  
Only full-body images without any occlusion or truncation are used to generate the dataset. However, the distance of pedestrians in raw images from surveillance cameras varies significantly due to their relative position against the camera; the smallest image has \(24\times 38\times 3\) resolution (farthest pedestrian), and the largest image having \(278\times 432\times 3\) (closest pedestrian). These images are resized to the arithmetic mean resolution found in the dataset, which is found to be \(64\times 160\times 3\). A set of example images in the dataset can be seen in Figure 3. The dataset is split between the anomaly cluster and normal cluster for each specific case, which are child anomaly vs. adult + elderly normal, adult anomaly vs. child + elderly normal, and elderly anomaly vs. child + elderly normal. During the training phase, the model is only trained with 80% of the normal-designated class. The remaining 20% of the normal-designated class and the anomaly class with an equal number of images are used during the testing phase. For instance, the proposed model is initially trained with 80% of the adult + elderly. The remaining 20% of images and the equal number of images from the anomalous class, child, are used for the inference. The detailed information about the train-test split can be found in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Classes** & **Number of Images** \\ \hline Elderly & 2809 \\ Adult & 4477 \\ Child & 1922 \\ Total & 9208 \\ \hline \end{tabular}
\end{table}
Table 1: The dataset information about the total number of images for each class.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Train**} & \multicolumn{4}{c}{**Test**} \\ \cline{2-9}
**Cases** & **Adult** & **Child** & **Elderly** & **Total** & **Adult** & **Child** & **Elderly** & **Total** \\ \hline Anomaly (Child) vs. Normal (Adult + Elderly) & 2247 & - & 3581 & 5828 & 562 & 1458 & 896 & 2916 \\ Anomaly (Adult) vs. Normal (Child + Elderly) & - & 1537 & 3581 & 5118 & 1281 & 385 & 896 & 2562 \\ Anomaly (Elderly) vs. Normal (Adult + Child) & 2247 & 1537 & - & 3784 & 562 & 385 & 947 & 1894 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The train-test split of the dataset. A total of 80% of two normal-designated class images are used in a cluster during the training without labels, and the remaining 20% of each normal-designated class, as well as the equal-numbered anomaly-designated class images, are used during the inference. Anomaly-designated class images are not used during the training.