# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 4 Experiments
### Qualitative Results  
The results obtained with the CDnet2014 dataset are shown in Figures 9 and 10. They exhibit the original marking (GT, ground truth, in green) used for training and the estimated contour (red points calculated by our method). These points mark the silhouettes of the pedestrians detected with the ASM algorithm. As can be seen in the scene exhibited in Figure 9, the algorithm fits the contour of the pedestrian very closely in the office scene because the lighting, and especially the background, allow precise detection. However, there are many problems in the PETS2006 scene (Figure 10a): the reduced scale of the pedestrian, occlusion from the train, and reflections from the floor (background problems); these problems cause ASM not to fit correctly. In the sofa scene (Figure 10b), there are also occlusion problems, especially the reflections from the floor, which create contrast problems, making it more difficult for ASM to perform a close fit. Despite the mentioned troubles, the pedestrian's silhouette was correctly identified in all cases.  
The results from the CASIA Gait dataset, where the pedestrian is walking in different orientations with respect to the camera, are shown in Figure 11. The pedestrian is shown walking at 0 degrees, i.e., directly to the camera, in Figure 11. The red points (approximate result) are very close to the landmarks (green points), which implies a good performance of the combination of MDBS + ASM. These were the best results of all orientations because there was not as much variation in the pedestrian's arms and legs. However, when the pedestrian is walking at 36 degrees (Figure 11), the red points in the arms (approximate contour) are moderately far from the true landmarks (green dots). In this scene, the pedestrian has variations in the movement of his arms, the opening of his legs, and his angle of walking (movement factors), which means that the ASM adjustment does not match the reference landmarks. Next, Figure 11 exhibits the adjustment results of a pedestrian walking at 54 degrees. The approximate points tend to be slightly off the landmarks in the legs, but the contour is more precise where the arms are closer to the body. Finally, the results of the adjustment of a pedestrian walking at 90 degrees are presented in Figure 11, where the pedestrian walks side-on to the camera. The fit results shown by the (approximate) red dots tend to be far from the landmarks on the legs. The adjustment by ASM in this scene beings the dots close to the contour of the pedestrian at all points except for the gap between the legs due to the movement.  
Good approximation of pedestrian detection is shown in Figures 9-11 due to the combination of MDBS and ASM from a qualitative point of view. The contour adjustment of a moving pedestrian is better when background problems (occlusion or illumination) are not evident and the factor of movement associated with the pedestrian is not too pronounced (Figure 9). In contrast, in situations with background problems (Figure 10) or too many movement factors (Figure 11), the detection with MDBS and the adjustment to the contour with ASM showed limited (but acceptable) performance; i.e., the closeness  
Figure 10: Detection of a pedestrian in selected scenes of the CDnet2014 collection. (**a**) PETS2006 scene, frame 1128. (**b**) Sofa scene, frame 108.  
Figure 9: Detection of a pedestrian in selected frames of the CDnet2014 office scene. “GT” represents the manually marked landmarks, and “Approx” shows the estimated landmarks. (**a**) Frame 630; (**b**) Frame 634.  
between the real landmarks and those obtained by the approximation is not as good as expected, but the pedestrian was correctly identified, and the contour is well defined.