基于Harris属性的运动摄像机视频中运动物体的识别

A. Nozari, S. Hoseini
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引用次数: 1

摘要

运动目标检测是图像处理中最重要的问题之一。它最近引起了许多关注。在本文中,也假设相机是移动的。以往的移动车辆检测方法主要采用雷达信号。对于在线运动目标检测,我们建议对从图像中提取的属性进行分层划分。每个移动的物体对应一个分区。与传统的分割算法不同,该方法的阈值距离不是固定的。这个阈值由高斯分布调节。应用Harris属性捕获角的属性。实验表明,该方法优于其他有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognition of Moving Objects in Videos of Moving Camera with Harris Attributes
Moving object detection is one of the most essential problems in image processing. It attracts many attentions recently. In the paper it is also assumed that the camera is moving. Major part of previous moving car detection methods engages radar signals. For online moving object detection, we suggest to employ hierarchical partitioning over the attributes extracted from image. Each moving object corresponds to a partition. Unlike the traditional partitioning algorithms, the threshold distance in the suggested method is not fixed. This threshold value is tuned by a Gaussian distribution. Harris attributes are applied to capture the corner attributes. Experimentations show the suggested method outperforms other competent methods.
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