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A Multiframes Integration Object Detection Algorithm Based on Time-Domain and Space-Domain

机译:基于时域和空间域的多帧集成对象检测算法

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摘要

In order to overcome the disadvantages of the commonly used object detection algorithm, this paper proposed a multiframes integration object detection algorithm based on time-domain and space-domain (MFITS). At first, the consecutive multiframes were observed in time-domain. Then the horizontal and vertical four-direction extension neighborhood of each target pixel were selected in space-domain. Transverse and longitudinal sections were formed by fusing of the time-domain and space-domain. The mean and standard deviation of the pixels in transverse and longitudinal section were calculated. We also added an improved median filter to generate a new pixel in each target pixel position, eventually to generate a new image. This method is not only to overcome the RPAC method affected by lights, shadows, and noise, but also to reserve the object information to the maximum compared with the interframe difference method and overcome the difficulty in dealing with the high frequency noise compared with the adaptive background modeling algorithm. The experiment results showed that the proposed algorithm reserved the motion object information well and removed the background to the maximum.
机译:为了克服常用的对象检测算法的缺点,本文提出了一种基于时域和空间域(MFITS)的多帧集成对象检测算法。首先,在时域中观察到连续的多帧。然后在空间域中选择每个目标像素的水平和垂直四方向扩展邻域。通过熔断时间域和空间域形成横向和纵向部分。计算横向和纵向部分中的像素的平均值和标准偏差。我们还添加了一种改进的中值滤波器来在每个目标像素位置生成新像素,最终生成新图像。该方法不仅克服受灯,阴影和噪声影响的RPAC方法,还与帧间差分方法相比将对象信息保留到最大值,并克服与自适应相比处理高频噪声的难度背景建模算法。实验结果表明,所提出的算法良好地保留了运动对象信息并将背景删除到最大值。

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