# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 5 Conclusions  
The performance of a combination of MDBS with ASM for detecting moving pedestrians was studied in this work. In previous works, ASM has been reported as an appropriate technique for adjusting people's silhouettes in static poses [9]. For this reason, it is relevant to explore its possibilities in the detection of people in motion. Our approach uses MDBS to locate pedestrians in motion; then, the silhouette of the located pedestrian is fitted with ASM. Background subtraction allows the detection of a moving region in a video sequence;  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Scene** & **Mean Error** & **Skewness** & **Kurtosis** & **JB** & _p-Value_ \\ \hline Office & 4.5 & 1.220 & 2.375 & 13.212 & 0.00135 \\ PETS 2006 & 8.3 & 0.769 & 0.928 & 13.868 & 0.00097 \\ Sofa & 7.2 & 0.916 & 0.498 & 20.031 & 0.00004 \\ \hline Pedestrian 0 degrees & 4.5 & 1.268 & 2.492 & 13.940 & 0.00094 \\ Pedestrian 36 degrees & 6.4 & 1.260 & 3.200 & 13.486 & 0.00118 \\ Pedestrian 54 degrees & 6.2 & 1.390 & 2.680 & 16.39 & 0.00028 \\ Pedestrian 90 degrees & 5.6 & 1.090 & 1.170 & 17.03 & 0.00020 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the Jarque–Bera test for the selected datasets.  
Figure 14: Mean fit error (in pixels) for each frame in the (**a**) Office, (**b**) PETS2006, and (**c**) Sofa scenes.  
Figure 15: Mean fit error for each frame in the CAISA Gait collection for pedestrians walking at (**a**) 0 degrees, (**b**) 36 degrees, (**c**) 54 degrees, and (**d**) 90 degrees.  
then, image transformations, namely, translation, rotation, scaling, and especially translation coordinates, are necessary to adjust the silhouette of the pedestrian via the active shape model.  
The primary contribution of this work was exploring the improvement potential of the combination of MDBS and ASM in detecting the contour of a moving pedestrian walking in a controlled environment. MDBS + ASM is a straightforward method based on classical algorithms which have shown effectiveness in pedestrian detection. We are looking for a practical process that could work in real applications, which is why we established our approach based on simple techniques.  
Experimental results showed that the combination of MDBS and ASM achieves good performance in detecting moving pedestrians; however, it is slightly affected in situations where evident background problems exist (occlusion or lighting) or when pronounced movements of either arms or legs are present. The future of this work is to search for new mechanisms to improve the capacity of the MDBS + ASM proposal for the detection of moving pedestrians under challenging scenarios, such as those with background problems or movement factors, leading to its application in diverse environments.  
Conceptualization, J.A.A.V.; formal analysis, R.A.E.; investigation, J.A.A.V.; methodology, R.A.E. and E.R.L.; project administration, M.R.H.; software, F.D.R.L.; supervision, R.A.E. and E.R.L.; writing--review and editing, E.E.G.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.