## 4 Results
### Third Scenario: Video with Pedestrians Waiting for the Bus  
In the third scenario, the video (Figure 16) shows several people waiting for the bus. Additionally, one person walks away from the bus stop, and at certain moments in the video, people are seen getting on the bus (indicated by the red circle in frames 253 and 480 in Figure 16). The dominant activity throughout the video is likely \"standing\" as people wait for the bus. The model tracked subtle changes in pose, such as adjusting posture or shifting weight, but the general activity remains standing still. Figure 17 shows the pose estimation for the people present in the video. It can be observed that AlphaPose is able to correctly estimate the skeleton and pose of all the individuals in the video, even when they are far away in the scene or boarding the bus. Additionally, AlphaPose can estimate the pose in frames where there is occlusion from objects or other people, which is crucial for maintaining accuracy in complex, crowded environments like bus stops. Using pose estimation data and feature vectors, the ML-RF model predicts the activities of each person. Figure 17 illustrates the activities recognized by the model across the video. The model successfully identified 7 activities throughout the video:  
* Knee fall
* Falling backward
* Using an object  
Figure 16: Pedestrians waiting for the bus at a typical Valparaiso bus stop. Figure 18 shows the activities performed by all the people in the video for each frame. As we can see, the ML-RF model occasionally recognizes activities that do not actually occur, such as different forms of falling (e.g., falling to the knees or falling backward). These misclassifications can be misleading, especially when the person is simply adjusting their posture or making a small movement, like getting ready to board the bus or shifting their weight. However, the model mistakenly identifies movements like falling when there may be no fall at all. These errors might occur when the person's pose shifts rapidly, or if there is some form of occlusion (e.g., the person moves behind an object or another person), making the model interpret the movement as a fall. In some cases, false positives occurred due to AlphaPose estimating poses for individuals who were too far from the camera or partially occluded, leading to incorrect skeletons. These noisy inputs caused the ML-RF model to misclassify activities, especially falls. However, this behavior is not indicative of model overfitting, as the model was trained with limited fall data, used balanced class distributions, and employed 10-fold cross-validation (90:10) to ensure generalization. These errors are primarily due to environmental complexity and limitations in the pose estimation process. As in the first and second cases, to identify the relevant activities, the predictions made by the ML-RF model are normalized and approximated for every 60 frames (1 s). Activities that do not actually occur in the video, such as \"falling\", are given very little weight in  
Figure 17: Frequency of activities recognized by the ML-RF model throughout the video. each 60-frame block. This helps filter out irrelevant activities and focus on the meaningful actions that are present in the scene. Figure 19 illustrates the normalized and approximated activities for each second of the video. By visualizing these data, we can see the transitions between the activities that were correctly recognized and those that were likely misclassified. For example, the \"falling\" activity will have very little weight in the graph, showing that the model detected it as a rare or irrelevant event, whereas activities like standing and walking will have a higher weight, indicating that they occurred more frequently and were accurately identified. This process provides insight into how often certain activities occurred, making it easier to analyze the overall behavior of pedestrians in the video.