Robust multi-pose face tracking by multi-stage tracklet association
Robust multi-pose face tracking by multi-stage tracklet association
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发表时间：
2012-11
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通讯作者：
M. Roth;M. Bäuml;R. Nevatia;R. Stiefelhagen
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作者：
M. Roth;M. Bäuml;R. Nevatia;R. Stiefelhagen
我们提出了一种多姿态人脸跟踪的方法，在两个阶段使用多个线索的人脸检测响应的关联。低级阶段使用双阈值策略来合并基于位置、大小和姿态的检测响应，从而产生短但可靠的轨迹。高级阶段使用不同的线索来计算轨迹片段之间的联合相似性度量。面部线索比较成对轨迹中最正面面部检测的面部特征。分类器线索学习每个轨迹的区别性外观模型，使用可靠轨迹内和重叠轨迹之间的检测对作为训练数据。约束提示观察两个轨迹的运动的兼容性。轨迹的关联是全局优化的匈牙利算法。我们在两部电视剧的两个具有挑战性的剧集上验证了我们的方法，并报告了多目标跟踪准确率（MOTA）分别为82%和68.2%。
We propose an approach for multi-pose face tracking by association of face detection responses in two stages using multiple cues. The low-level stage uses a two-threshold strategy to merge detection responses based on location, size and pose, resulting in short but reliable tracklets. The high-level stage uses different cues for computing a joint similarity measure between tracklets. The facial cue compares facial features of the most frontal face detections in pairs of tracklets. The classifier cue learns a discriminative appearance model for each tracklet, using detection pairs within reliable tracklets and between overlapping tracklets as training data. The constraint cue observes the compatibility of motion of two tracklets. The association of tracklets is globally optimized with the Hungarian algorithm. We validate our approach on two challenging episodes of two TV series and report a Multiple Object Tracking Accuracy (MOTA) of 82% and 68.2%, respectively.