Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background

Amr Almaddah, Tauseef Ahmad, A. Dubai
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Abstract

The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant.  In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features.  The goal of this work is to detect the corner of an object through the Harris corner detector with multiple scale of the same image. The scale invariant property applied to the Harris algorithm for improving the corner detection performance in different resolution of the same image with the same interest point. The detected points represented by two independent variables (x, y) in a matrix (x, y) and the dependent variable f are called intensity of interest points. Through these independent variable, we get the displacement and velocity of object by subtracting independent variable f(x,y) at current frame from the previous location f ̀((x,) ̀(y,) ̀) of another frame. For further work, multiple of moving object environment have been taken consideration for developing algorithms.
静止背景下单个运动物体位移和速度的检测与测量
传统的哈里斯探测器由于没有尺度不变性,对噪声和分辨率都比较敏感。在本研究中,对Harris角点检测器算法进行了改进,使其能够适用于多分辨率图像,该技术还通过使用直方图均衡化技术在光照条件较差的情况下工作。我们所做的工作解决了特征点的鲁棒性检测问题,检测到的多个局部特征在水平方向和垂直方向上的强度变化被称为角点特征。本工作的目标是通过Harris角点检测器对同一图像的多个尺度进行角点检测。利用Harris算法的尺度不变性,提高了具有相同兴趣点的同幅图像在不同分辨率下的角点检测性能。由矩阵(x, y)中的两个自变量(x, y)和因变量f表示的检测点称为兴趣点强度。通过这些自变量,从另一帧的前一位置f′((x,)′(y,)′)减去当前帧的自变量f(x,y,)′,得到物体的位移和速度。为了进一步的工作,在开发算法时考虑了运动目标环境的多样性。
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