Improved anti-occlusion tracking algorithm based on face detection

Jiawei He, Yingyun Yang
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Abstract

An improved anti-occlusion tracking algorithm based on face detection is elaborated in this paper. The Kalman filter and meanshift algorithm along with the coefficient of Bhattacharyya being used as a parameter for occlusion discovery consists of the framework of the base of this tracking system. Only occlusion takes place, will the face detection be involved in addition to the basic algorithm in the algorithm in even frames while the same pattern is implemented in odd frames as the none-occluded situation. Meanshift module is used without the interference of occlusion and face detection takes effects on the contrary in the way that linear prediction is provided by Kalman filter in both situation. The algorithm in this paper, testified from the experimental results, can effectively offset the instability brought by occlusion and recovery the trajectory of the objective instantaneously.
基于人脸检测的改进抗遮挡跟踪算法
本文阐述了一种改进的基于人脸检测的抗遮挡跟踪算法。卡尔曼滤波和meanshift算法以及以Bhattacharyya系数作为遮挡发现参数,构成了该跟踪系统的基础框架。当遮挡发生时,在偶数帧中除了算法中的基本算法外,还会涉及人脸检测,而在奇数帧中实现与无遮挡情况相同的模式。在没有遮挡干扰的情况下使用Meanshift模块,而人脸检测则以卡尔曼滤波提供线性预测的方式发挥相反的作用。实验结果表明,本文算法可以有效地抵消遮挡带来的不稳定性,并在瞬间恢复目标的轨迹。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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