We present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola-Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered; in addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola-Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. We also evaluate two recently published face detectors based on Convolutional Networks and Deformable Part Models, with our algorithm showing a comparable accuracy at a fraction of the computation time.
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机译:我们提出一种新颖的面部跟踪方法,其中将光流信息合并到Viola-Jones检测算法的修改版本中。在原始算法中,检测是静态的,因为不考虑来自先前帧的信息。另外,候选窗口必须通过分类级联的所有阶段,否则将被视为不包含面部而被丢弃。相反,建议的跟踪器保留有关每个窗口通过的分类阶段数的信息。此类信息用于构建似然图,该似然图表示脸部位于该位置的概率。通过通过光流计算将似然图的位置外推到下一帧来提供跟踪功能。所提出的算法可在标准笔记本电脑上实时工作。该系统在Boston Head Tracking Database上进行了验证,表明该算法在检测率和输出边界框的稳定性方面都优于标准的Viola-Jones检测器,并且具有处理遮挡的能力。我们还评估了基于卷积网络和可变形部分模型的两个最近发布的人脸检测器,我们的算法在计算时间的一小部分上显示出可比的准确性。
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