Autonomous target tracking by AUVs using dynamic vision

Yang Fan, Arjuna Balasuriya
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引用次数: 15

Abstract

Proposes a method for underwater target tracking using optical data, which consists of color, texture, shape and dynamic properties of the environment. Here, how the dynamic properties from image sequences can be used for target tracking by autonomous underwater vehicles (AUVs) is studied. Optical flow techniques are used to derive the dynamics of the images. The objects of interest are extracted from the images by using the dynamical properties and their optical features. The consecutive dynamic behavior of objects of interest is estimated based on the current dynamics. Using the predicted dynamics, the region of interest (ROI) in the image can be identified reducing the amount of data to be processed. This increases the speed of processing using the hardware available in small pressure hulls. Image dynamics and feature position information fused with other on-board sensor information, the navigational commands for the AUV are derived. In the paper, the extraction of objects of interest from the images based on dynamic vision is presented and the performance of this method for underwater target tracking is demonstrated by experimental results.
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基于动态视觉的auv自主目标跟踪
提出了一种利用由环境的颜色、纹理、形状和动态特性组成的光学数据进行水下目标跟踪的方法。本文研究了如何利用图像序列的动态特性对自主水下航行器进行目标跟踪。光流技术用于导出图像的动力学。利用图像的动力学特性和光学特征提取感兴趣的目标。基于当前动态估计感兴趣对象的连续动态行为。利用预测的动态,可以识别图像中的感兴趣区域(ROI),减少需要处理的数据量。这提高了处理的速度,使用硬件可用的小压力船体。将图像动态信息和特征位置信息与其他机载传感器信息融合,推导出水下航行器的导航命令。本文提出了一种基于动态视觉的水下目标目标提取方法,并通过实验验证了该方法在水下目标跟踪中的性能。
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