## 5 Implementation
### Recognition and Tracking Module
#### 5.3.4. Tracking
## 7 Conclusions and Future Work

This article presented a system for detecting pedestrian movement patterns, based on computational intelligence for image processing and pattern detection. The proposed system is capable of processing in real-time multiple image/video sources. An architecture based on pipes and filters is used to allow an easy evaluation of different computational intelligence techniques in each stage of the processing. Two main stages are identified in the system, focusing on extracting relevant features of the processed images (implemented in the Recognition and Tracking module) and detecting movement patterns (implemented in the Pattern Detection module). Several techniques are applied for image processing and pattern detection. The proposed implementation fulfills important requirements for a pedestrian movement patterns detection system: it is cross-platform, open-source, and efficient. The experimental analysis performed over more than 1450 problem instances covers the two main stages of the system. The system was evaluated using PETS09-S2L1 videos and the results were compared with part of the MOTChallenge benchmark results. Results suggest that the proposed system is competitive, yet simpler, than other similar software methods. Figure 6: Scene where the second street robbery occurs (it was not detected by the system). The main lines for future work include studying other algorithms for person detection that maintain the low processing cost and possibly improve the dynamical adaptation of blobs aspect ratios; and studying the auto-adaptation of parameters for different scenarios. In turn, to better handle scenes from foggy/rainy days, including support for scenes with different light conditions, and the integration of a specific module to automatically extract/learn the sequence of primitives from particular videos. Filters and estimators (e.g., velocity, depth) can be integrated to deal with the detection of people using moving cameras and other problems that emerge when background subtraction is not directly applicable. The integration of such modules is easily supported by the flexible pipes and filters architecture. Finally, the experimental analysis should be extended to account for the performance of the proposed system to deal with crowded scenarios. Further details about the proposed system are available on the project website fing.edu.uy/inco/grupos/cccal/hpc/APMP. J.C.: conceptualization, methodology, software, validation, research, writing; S.N. : conceptualization, methodology, software, validation, research, writing, supervision; A.T.: conceptualization, methodology, research, writing; V.S. : conceptualization, methodology, research, writing; V.S. : conceptualization, methodology, research, writing. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Part of the research was developed in project \"Algorithmos de inteligencia computacional para deteccion de patrones de movimiento de personas\" by [PERSON], [PERSON] and [PERSON] (advisor, S.N.). The authors declare no conflict of interest. ## References

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