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A new method of moving object detection using adaptive filter

机译：利用自适应滤波器的运动目标检测新方法

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摘要

In many real-world video analysis systems , the available resources are constrained, which limits the image resolution. However, the low computational complexity and fast response for low-resolution images still make them attractive for computer vision applications. This work presents a new model that uses a least-mean-square scheme to train the mask operation for low-resolution images. This efficient and real-time method, which uses an adaptive least-mean-square scheme (ALMSS), uses the training mask to detect moving objects on resource-limited systems. The detection of moving objects is a basic and important task in video surveillance systems, which affects the results of any post-processing, such as object classification, object identification and the description of object behaviors. However, the detection of moving objects in a real environment is a difficult task because of noise issues, such as fake motion or noise. The ALMSS method effectively reduces computational cost for both fake motion environment. The experiments using real scenes indicate that the proposed ALMSS method is effective in the real-time detection of moving objects. This method can be implemented in hardware for high-resolution applications, such as full-HD images. A prototype VLSI circuit is designed and simulated using a TSMC 0.18 mu m 1P6M process.
机译：在许多现实世界的视频分析系统中，可用资源受到限制，这限制了图像分辨率。但是，低分辨率图像的低计算复杂度和快速响应仍然使它们对于计算机视觉应用具有吸引力。这项工作提出了一个新模型，该模型使用最小均方方案训练低分辨率图像的遮罩操作。这种高效且实时的方法使用自适应最小均方方案（ALMSS），它使用训练掩码来检测资源受限系统上的运动对象。移动对象的检测是视频监视系统中的一项基本且重要的任务，它会影响任何后处理的结果，例如对象分类，对象标识和对象行为描述。然而，由于诸如假动作或噪声之类的噪声问题，在真实环境中检测运动物体是一项艰巨的任务。 ALMSS方法有效地降低了两种假运动环境的计算成本。使用真实场景的实验表明，所提出的ALMSS方法在运动物体的实时检测中是有效的。该方法可以在高分辨率应用（例如全高清图像）的硬件中实现。使用台积电0.18微米1P6M工艺设计和仿真了VLSI原型电路。

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