ERC — An environmental robust Camshift face tracking algorithm

Dajie Cong, Ping Shi, Yang Li
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Abstract

The classical Camshift face tracking algorithm requires a higher environmental demand and is susceptible to the skin color interference. To solve the above problems, an improved Camshift tracking algorithm - ERC (Environmental Robust Camshift) is presented. Firstly, the RGB histogram equalization is applied in ERC to extend the hue differentiation between pixels; secondly, the statistical and spatial information are combined by using the adaptive kernel function based on the spatiogram to obtain the target tone model; finally, the tracking module in the classical Camshift algorithm is employed to realize the target tracking. The experiment results show that the ERC can achieve an effective face tracking in the case of intense illumination changes, background clutter and rapid movement.
环境鲁棒Camshift人脸跟踪算法
传统的Camshift人脸跟踪算法对环境要求较高,易受肤色干扰。为了解决上述问题,提出了一种改进的Camshift跟踪算法——ERC (Environmental Robust Camshift)。首先,在ERC中应用RGB直方图均衡化来扩展像素间的色相区分;其次,利用基于空间图的自适应核函数将统计信息与空间信息相结合,得到目标色调模型;最后,利用经典Camshift算法中的跟踪模块实现目标跟踪。实验结果表明,在光照变化强烈、背景杂波和快速运动的情况下,ERC可以实现有效的人脸跟踪。
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