Parameters Improvement of Gaussian Mixture Model for Vehicle Detection in Different Weather Conditions
DOI:
https://doi.org/10.22452/Keywords:
Gaussian Mixture Model, Adaptive Time-varying Learning Rate, Exponential Decay, Outlier Processing, Vehicle DetectionAbstract
Accurate vehicle detection under different weather conditions is crucial to ensure efficient and safe traffic management systems. This paper presents a novel approach for vehicle detection in different weather scenarios, leveraging on Improved Gaussian Mixture Model (Improved GMM). Traditional methods often falter in different weather conditions, posing significant challenges to traffic management. The modified model addresses these challenges by integrating advanced features such as adaptive time-varying learning rates, exponential decay, and outlier processing. This ensures robust performance across different weather conditions, including daytime, nighttime, and rainy weather. Real-world datasets evaluate the model's efficacy, simulating various weather scenarios to assess its adaptability and reliability. Comparative analysis demonstrates the superiority of the Improved GMM over traditional techniques, exhibiting enhanced accuracy and robustness in different weather conditions while maintaining computational efficiency. The findings highlight the potential of the modified model to significantly improve vehicle detection accuracy in different weather conditions, thus contributing to the advancement of reliable traffic management systems irrespective of environmental challenges.
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