Exploiting visual quasi-periodicity for real-time chewing event detection using active appearance models and support vector machines
Exploiting visual quasi-periodicity for real-time chewing event detection using active appearance models and support vector machines
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DOI：
10.1007/s00779-011-0425-x
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发表时间：
2012-08-01
影响因子：
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通讯作者：
Helal, Abdelsalam
中科院分区：
文献类型：
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作者：
Cadavid, Steven;Abdel-Mottaleb, Mohamed;Helal, Abdelsalam
医疗保健费用和肥胖的稳步增长激发了最近对能够监测饮食习惯的经济高效的辅助系统的研究。然而，很少有研究人员研究过使用视频作为监测饮食活动的手段。视频具有多种固有特性，例如被动采集，值得将其作为此类应用的输入模式进行分析。为此，我们提出了一种自动检测对象监控视频中咀嚼事件的方法。首先，使用主动外观模型 (AAM) 来跟踪视频序列中主体的面部。据观察，咀嚼事件中 AAM 参数的变化表现出明显的周期性。我们利用这一特性来区分咀嚼和非咀嚼的面部动作，例如说话。通过将谱分析应用于模型参数值的时间窗口来构建特征表示。估计的功率谱随后经历非线性降维。采用功率谱的低维嵌入来训练二元支持向量机分类器来检测咀嚼事件。为了模拟咀嚼的逐渐开始和偏移，对相邻视频帧的类别预测施加平滑度，以阻止类别标签的突然变化。实验在一个由 37 名受试者组成的数据集上进行，这些受试者执行五种动作，即张嘴和闭嘴咀嚼、杂乱的面孔、说话和静止的面孔。实验结果产生了 93.0% 的交叉验证百分比一致性，表明所提出的系统提供了一种有效的自动咀嚼检测方法。
Steady increases in healthcare costs and obesity have inspired recent studies into cost-effective, assistive systems capable of monitoring dietary habits. Few researchers, though, have investigated the use of video as a means of monitoring dietary activities. Video possesses several inherent qualities, such as passive acquisition, that merits its analysis as an input modality for such an application. To this end, we propose a method to automatically detect chewing events in surveillance video of a subject. Firstly, an Active Appearance Model (AAM) is used to track a subject's face across the video sequence. It is observed that the variations in the AAM parameters across chewing events demonstrate a distinct periodicity. We utilize this property to discriminate between chewing and non-chewing facial actions such as talking. A feature representation is constructed by applying spectral analysis to a temporal window of model parameter values. The estimated power spectra subsequently undergo non-linear dimensionality reduction. The low-dimensional embedding of the power spectra are employed to train a binary Support Vector Machine classifier to detect chewing events. To emulate the gradual onset and offset of chewing, smoothness is imposed over the class predictions of neighboring video frames in order to deter abrupt changes in the class labels. Experiments are conducted on a dataset consisting of 37 subjects performing each of five actions, namely, open- and closed-mouth chewing, clutter faces, talking, and still face. Experimental results yielded a cross-validated percentage agreement of 93.0%, indicating that the proposed system provides an efficient approach to automated chewing detection.