# A Road Behavior Pattern-Detection Model in Queretaro City Streets by the Use of Shape Descriptors
## 2 Materials and Methods
### Features Extraction
Figure 11: Cause–effect relationship in distant cameras with different states: (**a**) objects that are just reaching the camera \(c_{3}\); (**b**) moving objects and how they are seen on the camera \(c_{2}\). Figure 12: The particular case of high positive correlation: (**a**) attributes that describe the correlation between two cameras; (**b**) illustration of regions between different cameras and their correlation; (**c**) experimental scenario showing the direction of flow and correlation; (**d**) trend graph of the geometric variables area and perimeter that reference to correlation behavior. ## 4 Discussion

In terms of application, this research showed that, by using feature extraction, it is possible to find the relationships between a set of urban variables (traffic, pedestrians, traffic light timing, and road obstructions, to name a few), measured by a fixed zone monitoring system, resulting in the generation of a stochastic causal model using graphs. The proposed model generates positive and negative dependence relationships with an average accuracy of 78%, which provides the information to interpret the cause-effect relationships and allows the detection of atypical behaviors, helping to better understand the dynamics of the monitoring scenario. Existing algorithms in the literature were implemented to detect the local behavior of objects in each camera. In conjunction, these results can be obtained by implementing the stochastic model on general-purpose electronic cards, which can be applied for inferences and modeling of periodic behaviors of scenarios in smart cities. The originality consists of finding the temporal and spatial dependencies between the behavior of a particular camera and the other adjacent cameras in the study scenario. ## 5 Conclusions

This research consisted of finding the time relationship \(R(I_{K},I_{L})\) for \(I_{K},I_{L}\in c_{i}\), and constructing the associated graph for this relationship; the relationship \(R\) has associated with it a high probability \(P(R)\) of being observed for an interval \(tt\subset 0\leq t\leq T\). If one time series causes another, knowledge of the first process will help to predict the future values of the other after the influences of other variables have been taken into account. The fundamental contribution is to automatically find the \(R\) relationships using the evidence observed by a particular camera \(c_{i}\). Therefore, the method for finding \(R\) is to employ a graph-based causal model. Conceptualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; methodology, [PERSON]-[PERSON] and [PERSON]-[PERSON]; software, [PERSON]-[PERSON] and [PERSON]-[PERSON]; validation, [PERSON]-M. and [PERSON]-H.; formal analysis, [PERSON]-M. and [PERSON]-H.; investigation, [PERSON]-M.; resources, [PERSON]-[PERSON] and [PERSON]; data curation, [PERSON]-M.; writing--original draft preparation, [PERSON]-[PERSON]; writing--review and editing, [PERSON]-[PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available at: [[https://github.com/trejon1/patterndetectionmodel](https://github.com/trejon1/patterndetectionmodel)]([https://github.com/trejon1/patterndetectionmodel](https://github.com/trejon1/patterndetectionmodel)) (accessed on 20 May 2024). The source code was typed in Python 3 using Jupyter Notebook. Thanks to the UAQ Informatics faculty, CIDESI, and CONAHCYT for the facilities for the development of this work (materials used for experiments). The authors wish to thank the anonymous reviewers for their positive and valuable comments to improve and refine this work. The authors declare no conflicts of interest. ## References

* [PERSON] and [PERSON] (2013) [PERSON] [PERSON]; [PERSON] [PERSON] A survey on activity recognition and behavior understanding in video surveillance. _Vis.