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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Adaptive GMM and BP Neural Network Hybrid Method for Moving Objects Detection in Complex Scenes
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Adaptive GMM and BP Neural Network Hybrid Method for Moving Objects Detection in Complex Scenes

机译：复杂场景中运动目标检测的自适应GMM和BP神经网络混合方法

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摘要

Moving foreground objects detection in complex scenes is a tough job because it requires high recognition accuracy. Adaptive Gaussian mixture model (AGMM) can be used to extract the foreground objects and it shows good performance, however, the detection quality of the foreground objects under complex scenes is not excellent. In this paper, an AGMM and BP neural network hybrid method is proposed, which is used to extract the foreground objects in complex scenes such as, dynamic backgrounds, illumination changes and moving shadows. In this method, an improved BP neural network is used to post-process the images of the foreground objects that are extracted from the AGMM. The neural network has strong robustness by learning the statistical features of the images. Momentum term and adaptive learning rate are added in the BP neural network algorithm to improve the training speed and robustness of the network. The experimental results show that the proposed AGMM and BP neural network hybrid method can extract the complete foreground objects effectively when compared with some other moving objects detection algorithms.
机译：在复杂场景中移动前景物体检测是一项艰巨的任务，因为它需要很高的识别精度。自适应高斯混合模型（AGMM）可用于提取前景物体，表现出良好的性能，但是，复杂场景下前景物体的检测质量并不理想。本文提出了一种AGMM和BP神经网络混合方法，该方法用于提取动态背景，光照变化和运动阴影等复杂场景中的前景物体。在这种方法中，使用改进的BP神经网络对从AGMM中提取的前景对象的图像进行后处理。通过学习图像的统计特征，神经网络具有强大的鲁棒性。在BP神经网络算法中增加了动量项和自适应学习率，以提高训练速度和网络的鲁棒性。实验结果表明，与其他运动目标检测算法相比，提出的AGMM和BP神经网络混合方法能够有效地提取出完整的前景目标。

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