# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 4 Experiments
### Quantitative Results
The best performance of the MDBS + ASM proposal was observed for the sofa and office scenes and was slightly worse for PETS2006, because of the reduced size of the captured pedestrian, but also due to the background problems and occlusion with the train in the scene. In all cases, the error is small in comparison to the size of the original frames. From a general point of view, Figure 12 shows that the proposed approach had a better performance in the Office scene than in PETS2006 or Sofa scenes (in this order, respectively). This is explained by the Office scene having less movement and low illumination variations (only when the pedestrian enters the office and is near a piece of furniture or when the pedestrian is reading a book). In the PETS2006 scene, the MDBS + ASM method presents the most significant errors when the camera is far from the objective (the pedestrian is at the extreme of the stage); also, the camera makes some adjustments due to the brightness of the floor or the occlusion that exists with background objects (such as a train that passes behind the pedestrian). Similarly, in the Sofa scene, the natural and artificial lighting affects the brightness of the floor, and it causes occlusion when the pedestrian passes near the sofa. Furthermore, opening and closing their feet and hands (movement variations) contributes to worsening the algorithm's performance (compared to the other scenes). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Scene** & **Min** & **Max** & **Median** & **Range** & **IQR** & **Q1** & **Q2** & **Q3** \\ \hline Office & 3.0 & 6.5 & 4.5 & 3.5 & 1.5 & 3.8 & 4.5 & 5.3 \\ PETS 2006 & 3.7 & 10 & 5.9 & 6.2 & 3.2 & 4.8 & 5.9 & 8.0 \\ Sofa & 2.8 & 8.9 & 6.1 & 6.1 & 3.4 & 3.9 & 6.1 & 7.3 \\ \hline Pedestrian 0 degrees & 3.0 & 6.5 & 4.5 & 3.5 & 1.4 & 3.8 & 4.5 & 5.2 \\ Pedestrian 36 degrees & 3.7 & 10.1 & 5.9 & 6.4 & 3.4 & 4.6 & 5.9 & 8.0 \\ Pedestrian 54 degrees & 3.0 & 9.9 & 6.2 & 6.9 & 3.3 & 3.9 & 6.2 & 7.2 \\ Pedestrian 90 degrees & 2.9 & 11 & 5.1 & 8.0 & 3.9 & 3.3 & 5.1 & 7.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the main characteristics of data shown in the box-plot diagrams of Figures 12 and 13. For the evaluation of scenes in the CASIA Gait dataset, where pedestrians walk at different orientations relative to the camera, the results are shown in Figure 13. In this collection of video scenes, all frames have a resolution of 320 \(\times\) 240 pixels. The number of evaluated frames was set to \(M=50\), \(L=50\) landmarks were used, the size of each one of the landmarks was 40 pixels, and the analysis was done twice, as it was for the CDnet2014. In the scene where the pedestrian walk at zero degrees, a mean fit error of 4.5 pixels was calculated (the overall best result in our experiments). Tegarding the pedestrian walking at 36 degrees, the mean fit error was 6.4 pixels; in the case of the scene at 54 degrees, the error was 6.1 pixels; lastly, in the scene with a pedestrian walking at 90 degrees, the error was 5.6 pixels. Higher error was noted when the pedestrian opened his legs, or variations in the movement of the arms were detected. However, the excess or lack of illumination or occlusions (background problems) also affect the performance of the MDBS + ASM technique because they avoid correctly detecting the pedestrian silhouette. The best performance observed in Figure 13 was found for scenes where the position of the camera showed less movement variation. In the scene of the pedestrian walking at zero degrees, the shot is from the front, and the movement of hands and feet is not perceptible by the camera, so there was reduced error at the time of detection. However, in the scene with a pedestrian walking at 36 degrees, the camera angle is a factor when it focuses on the pedestrian moving his arms and legs when walking. In the scene of a pedestrian walking at 54 degrees, like the previous one, the camera's position influences the detection, although there is less variation in the movement of the arms and legs.