# Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments
## 3 Materials and Methods
### Segmentation Criteria
Our experimental validation exhibited benefits over conventional methods by ensuring lower false detection rates and maintaining high accuracy in object detection across a variety of challenging conditions. Despite its performance, our method encounters limitations when processing images of reduced size or in scenarios with complex lighting variations. The difficulty in characterizing such changes impacts the algorithm's performance, suggesting a need for improved strategies in handling small objects or subtle texture variations. Additionally, the reliance on specific Gabor filter parameters and the selection of an optimal threshold for background subtraction present complexities in parameter optimization, potentially restricting the method's adaptability and ease of implementation across diverse surveillance contexts. Looking forward, we aim to address these limitations by exploring adaptive parameterization techniques that can dynamically adjust the Gabor filter settings based on the scene's characteristics. This could enhance the method's robustness against varied image sizes and complex lighting conditions. Further, we plan to investigate deep learning frameworks that could learn these parameters autonomously, offering a more sophisticated understanding of the scene dynamics. Additionally, integrating multimodal data sources, such as depth information, could enrich the algorithm's contextual awareness, opening opportunities for more subtle object detection and background modeling. Through these advancements, we aspire to broaden the applicability of our method, making it a more versatile tool for real-time surveillance and motion tracking in an array of real-world settings. Conceptualization, H.J.-H. and J.-A.-R.-G.; methodology, [PERSON]-A.R.-G., D.-M.C.-E., J.T., A.-M.H.-N. and H.J.-H.; software, J.-A.R.-G.; validation, J.-A.R.-G., D.-M.C.-E. and J.T. ; formal analysis, J.-A.R.-G., D.-M.C.-E., J.T., A.-M.H.-N. and H.J.-H.; investigation, J.-A.R.-G., D.-M.C.-E., J.T. and H.J.-H.; resources, J.-A.R.-G., D.-M.C.-E., J.T., A.-M.H.-N. and H.J.-[PERSON]; writing--original draft preparation, [PERSON]-A.R.-G., [PERSON]-M.C.-E., [PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-A.R.-G., [PERSON]-M.C.-E., [PERSON], A.-M.H.-N. and [PERSON]-H.; visualization, [PERSON]-A.R.-G., D.-M.C.-E., J.T. and [PERSON]-H.; supervision, [PERSON]-A.R.-G., [PERSON]-M.C.-E., [PERSON], A.-M.H.-N. and [PERSON]-H.; project administration, [PERSON]-A.R.-G., [PERSON]-M.C.-E., [PERSON], [PERSON]-M.H.-N. and [PERSON]-H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. All the data are available in the manuscript. We thank the Autonomous University of Queretaro and the National Council of Humanities, Sciences, and Technologies (CONAHCYT) through doctoral scholarship. The authors declare no conflicts of interest. ## References

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