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Vision Based Navigation System of Intelligent Vehicles: A Robust Object Tracking Approach

机译:基于视觉的智能车辆导航系统:一种强大的物体跟踪方法

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Object tracking using vision technology is one of the important and complex functions in computer vision. It should become more challenge in case of there are partial occlusions and significant clutter. A mean shift procedure embedded approach for vehicle and pedestrian tracking under real road scenes is presents. A combined hue and saturation of HSI color model and local orientation information are extracted and constructed to represent the target features since it is more robust to illumination than only RGB color space. The mean shift procedure is employed to fast searching the mode of the potential object in a neighborhood of current video sequence.Meanwhile, a global motion compensation is considered for improve the robust and real-time of the tracking procedure.Some examples are investigated and used to evaluate the proposed approach. Experimental results demonstrate that the proposed approach is robust and validate in complicated real scenes.
机译:使用Vision技术的对象跟踪是计算机视觉中的重要且复杂的功能之一。如果存在部分闭塞和显着的杂乱,它应该变得更加挑战。在真正的道路场景下的车辆和行人跟踪的平均换档方法是礼物。提取和构造HSI颜色模型和局部方向信息的组合色调和饱和度以表示目标特征,因为它比仅比仅RGB颜色空间更稳健。用于快速搜索当前视频序列的附近的潜在对象的模式。虽然,所考虑改善跟踪过程的稳健和实时的全局运动补偿。研究并使用了全局运动补偿。评估所提出的方法。实验结果表明,所提出的方法在复杂的真实场景中是强大的,验证。

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