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Activity detection using Sequential Statistical Boundary Detection (SSBD)

机译：使用顺序统计边界检测（SSBD）进行活动检测

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摘要

The spiralling increase of video data has rendered the automated localization and recognition of activities an essential step for video content understanding. In this work, we introduce novel algorithms for detecting human activities in the spatial domain via a binary activity detection mask, the Motion Boundary Activity Area (MBAA), and in the time domain by a new approach, Statistical Sequential Boundary Detection (SSBD). MBAAs are estimated by analyzing the motion vectors using the Kurtosis metric, while dense trajectories are extracted and described using a low level HOGHOF descriptor and high level Fisher representation scheme, modeling a Support Vector Data Description (SVDD) hypersphere. SSBD is then realized by applying Sequential Change Detection with the Cumulative Sum (CUSUM) algorithm on the distances between Fisher data descriptors and the corresponding reference SVDD hyperspheres for rapid detection of changes in the activity pattern. Activities in the resulting video subsequences are then classified using an multi-class SVM model, leading to state of the art results. Our experiments with benchmark and real world data demonstrate that our technique is successful in reducing the computational cost and also in improving activity detection rates.
机译：视频数据的螺旋式增长使活动的自动定位和识别成为了解视频内容的必不可少的步骤。在这项工作中，我们介绍了一种新颖的算法，该算法通过二进制活动检测掩码运动边界活动区域（MBAA）在时域中检测人的活动，并通过统计连续边界检测（SSBD）新方法在时域中检测人的活动。通过使用峰度度量分析运动矢量来估计MBAA，同时使用低级HOGHOF描述符和高级Fisher表示方案提取并描述密集轨迹，并对支持矢量数据描述（SVDD）超球建模。然后，通过在Fisher数据描述符和相应的参考SVDD超球之间的距离上应用具有累积总和（CUSUM）算法的顺序变化检测来实现SSBD，以快速检测活动模式的变化。然后，使用多类SVM模型对所得视频子序列中的活动进行分类，从而获得最新的结果。我们使用基准数据和实际数据进行的实验表明，我们的技术成功地降低了计算成本并提高了活动检测率。

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