首页> 外文会议>2011 19th Iranian Conference on Electrical Engineering >A robust parametric active contour model for target tracking using modified energies: Virtual electric field and motion-based balloon
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A robust parametric active contour model for target tracking using modified energies: Virtual electric field and motion-based balloon

机译:使用修改后的能量进行目标跟踪的鲁棒参数主动轮廓模型:虚拟电场和基于运动的气球

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Active contour model (ACM) is a powerful tool for the target tracking in digital image sequences. Traditional ACM fails to track the targets when target displacements or aspect changes are high and inhomogeneous. In order to improve these inefficiencies, in this paper, a modified balloon energy in the parametric ACM is defined base on target displacement in two successive frames to inflate some suitable points adaptively and locally, in the current frame. Moreover, traditional ACM suffers from low capture range, so it can not attract to the concave boundaries of the target those results to an uncertain tracking in image sequences. To improve this inefficiency, a modified virtual electric field energy is used in conjunction with the proposed balloon energy. Also, new ACM algorithm adapted base on mutual changes of target and background gray levels. The advantages of the proposed ACM consist of: less sensitivity to initialization, large capture range, attraction to sharp-pointed and concave boundaries of target, ability of tracking the target with large and inhomogeneous displacements, and acceptable computational cost. Experimental results show that the tracking by the proposed ACM based on the greedy algorithm produces more correct detection percent of the target boundaries than other similar methods, in different circumstances.
机译:活动轮廓模型(ACM)是用于在数字图像序列中进行目标跟踪的强大工具。当目标位移或纵横比变化很大且不均匀时,传统的ACM无法跟踪目标。为了改善这些低效率,在本文中,基于两个连续帧中的目标位移在参数ACM中定义了修改后的气球能量,以在当前帧中自适应地和局部地膨胀一些合适的点。而且,传统的ACM捕获范围低,因此它不能吸引目标的凹形边界,从而导致图像序列中的不确定跟踪。为了提高这种效率,将修改后的虚拟电场能量与建议的气球能量结合使用。此外,新的ACM算法根据目标和背景灰度级的相互变化进行了调整。所提出的ACM的优点包括:对初始化的敏感性较低,捕获范围大,对目标的尖锐和凹形边界的吸引力,具有较大且不均匀位移的跟踪目标的能力以及可接受的计算成本。实验结果表明,在不同情况下,基于贪心算法的ACM跟踪方法比其他类似方法能更正确地检测目标边界。

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