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首页> 外文会议>2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications >Improved Background Subtraction-based Moving Vehicle Detection by Optimizing Morphological Operations using Machine Learning
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Improved Background Subtraction-based Moving Vehicle Detection by Optimizing Morphological Operations using Machine Learning

机译：通过使用机器学习优化形态学操作，改进了基于背景扣除的移动车辆检测

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摘要

Object detection represents the most important component of Automated Vehicular Surveillance (AVS) systems. Moving vehicle detection based on background subtraction, with fixed morphological parameters, is a popular approach in AVS systems. However, the performance of such an approach deteriorates in the presence of sudden illumination changes in the scene. To address this issue, this paper proposes a method to adjust in real-time the morphological parameters to the illumination changes in the scene. The method is based on machine learning. The features used in the machine learning models are first, second, third and fourth-order statistics of the grayscale images, and the outputs are the appropriate morphological parameters. The resulting background subtraction-based object detection is shown to be robust to illumination changes, and to significantly outperform the conventional approach. Further, artificial neural network (ANN) is shown to provide better performance than Naive Bayes and K-Nearest Neighbours models.
机译：对象检测代表了自动车辆监视（AVS）系统的最重要组成部分。具有固定形态参数的基于背景减法的运动车辆检测是AVS系统中的一种流行方法。但是，这种方法的性能在场景中突然出现照明变化的情况下恶化。为了解决这个问题，本文提出了一种根据场景中的光照变化实时调整形态参数的方法。该方法基于机器学习。机器学习模型中使用的特征是灰度图像的一阶，二阶，三阶和四阶统计量，并且输出是适当的形态参数。结果表明，基于背景扣除的对象检测对于照明变化具有鲁棒性，并且明显优于传统方法。此外，显示出的人工神经网络（ANN）比朴素贝叶斯（Naive Bayes）和K最近邻居模型具有更好的性能。

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