# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 4 Experiments
### Quantitative Results
Finally, in the scene of the pedestrian walking at 90 degrees, it is observed walking sideways and walking from left to right, and some errors occurred at the time of detection between the legs. Moreover, the pedestrian's clothing also affects the detection process, especially when pants are occluded by the floor squares (which can be almost the same as the pants, especially in the grayscale). To summarize, the complexity intrinsic to changes in the camera position causes varied performance of the MDBS + ASM model. For the above reasons, we consider it necessary to deeply study the method to improve its performance in complex scenarios. Figure 12: Mean fit error between the landmarks and the estimated contour in the office, PETS2006, and sofa scenes of CDnet2014 collection. To analyze if the mean fit error evaluated in the selected datasets has a normal distribution, the Jarque-Bera test was performed. It is a goodness-of-fit test of departure from normality based on the sample skewness and kurtosis. If a data distribution has a normal distribution, then the skewness (\(W\)) will be close to zero, and the kurtosis will be \(K=3\), ideally. From a collection of \(N\) observations, the statistic test \(JB\) is defined as follows [42]:  
\[JB=\frac{N}{6}(W^{2}+\frac{(K-3)^{2}}{4}) \tag{23}\]  
In our experiments, the number of observations (groups) could be assumed as the total of frames used for each scene, being \(N=50\). Then, the total of landmarks by frame \(L=50\) led to 2500 data points per scene, analyzed to estimate the \(JB\) significance value. The null hypothesis \(H_{0}:W=0\) and \(K=3\) is rejected if high values of \(JB\) are obtained; i.e., the observed data do not follow a normal distribution. Moreover, the probability (\(p\)-value) of the null hypothesis assumes the fact that \(JB\) is asymptotically distributed as a chi-Square function with two degrees of freedom [43], and the \(p\)-value could be estimated from classical probability tables [44] or standard spreadsheet software. Considering the right tail of the chi-square distribution, the null hypothesis can be rejected if the \(p\)-value \(<0.05\)[42]. In Table 2, the summary of the results of the Jarque-Bera normality test is exhibited. From the normality test, it can be assumed that the fit error in all datasets does not follow a normal distribution, which in this case implies that each landmark in each frame has the same probability of error, and there was not a trend or bias in the experiments. This can be visually seen in the graphs in Figure 14, which have the mean fit error by frame for the Office, PETS2006, and Sofa. Similarly, Figure 15 exhibits the behavior of the mean error by frame for the CAISA Gait collection for each pedestrian walking orientation. In all cases, the error varies from one frame to another, in the intervals specified in Table 1. The variations in the error are explained because, in each frame, different conditions could be encountered: lighting, occlusions, pose, and so on. However, the error size is always relatively low (in the range of 2.8 to 11 pixels) compared to the size of the frame (hundreds of pixels). Figure 13: Mean fit error between landmarks and the estimated contour for the CASIA Gait collection when the pedestrian walks at \(0,36,54\), or \(90\) degrees, relative to the camera.