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Robust visual object tracking with extended CAMShift in complex environments

机译:在复杂环境中使用扩展的CAMShift进行可靠的视觉对象跟踪

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This paper presents a new approach to solve the problem of real-time robust object tracking in complex environments. Generally, traditional CAMShift (Continuous Adaptive Mean Shift) provides speed and robustness for visual tracking, but it will become unstable when similar objects are presented in background or occlusion happens. In this paper, we proposed an advanced two level approach towards these problems with improved back-projection and kalman filter based occlusion handling. The lower level of the approach implements the multidimensional color histogram and the combination of color and motion information based improved histogram back-projection process. The higher level of the approach implements kalman filter based occlusion handling process with CAMShift. With this method, our proposed method robustly tracks the target object under rapid illumination changes, highly similar-colored background, and occlusion handling condition in real-time with high accuracy. The experimental results show that the proposed algorithm is robust and efficient to track the various object in indoor/outdoor complex environments
机译:本文提出了一种解决复杂环境中实时鲁棒对象跟踪问题的新方法。通常,传统的CAMShift(连续自适应均值移位)为视觉跟踪提供了速度和鲁棒性,但是当在背景中出现类似物体或发生遮挡时,它将变得不稳定。在本文中,我们针对这些问题提出了一种先进的两级方法,其中包括改进的反投影和基于卡尔曼滤波器的遮挡处理。该方法的较低级别实现了多维颜色直方图以及基于颜色和运动信息的组合,从而改进了直方图反投影过程。该方法的更高级别使用CAMShift实现了基于卡尔曼滤波器的遮挡处理过程。通过这种方法,我们提出的方法可以在高照度下实时,快速地跟踪目标对象,包括快速的照明变化,高度相似的颜色背景以及遮挡处理条件。实验结果表明,所提出的算法在室内/室外复杂环境下能够有效跟踪各种物体。

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