贝叶斯非参数子模块视频分割的鲁棒异常检测
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection
摘要：
多实例学习（MIL）提供了一种有效的方法来解决视频异常检测问题，通过将其建模为弱监督问题，因为标签通常仅在视频级别可用，而由于昂贵的标签成本而丢失帧。我们建议进行新的贝叶斯非参数子模块视频划分（BN-SVP），以显着改善MIL模型训练，可以提供一个高度可靠的解决方案，在实际设置中，包括离群片段或多种类型的异常事件的鲁棒异常检测。BN-SVP本质上执行动态非参数层次聚类，具有增强的自转换，将视频中的片段分组为可以自然地解释为场景的时间一致和语义一致的隐藏状态。假设每个片段是通过非参数混合过程生成的，该过程允许相同场景内的片段的变化以适应许多真实世界监控视频的动态和噪声性质。BN-SVP的场景和混合成分分配也引起段之间的成对相似性，从而导致子模集函数的非参数构造。将此函数与MIL损失集成，可以有效地将模型暴露给各种潜在的积极实例，以改进其训练。提出了一种贪心算法来优化子模函数，并支持有效的模型训练。我们的理论分析确保了所提出的算法的性能保证。在多个真实世界的异常视频数据集上证明了所提出的方法的有效性，具有鲁棒的检测性能。
英文摘要：
Multiple-instance learning (MIL) provides an effective way to tackle the video anomaly detection problem by modeling it as a weakly supervised problem as the labels are usually only available at the video level while missing for frames due to expensive labeling cost. We propose to conduct novel Bayesian non-parametric submodular video partition (BN-SVP) to significantly improve MIL model training that can offer a highly reliable solution for robust anomaly detection in practical settings that include outlier segments or multiple types of abnormal events. BN-SVP essentially performs dynamic non-parametric hierarchical clustering with an enhanced self-transition that groups segments in a video into temporally consistent and semantically coherent hidden states that can be naturally interpreted as scenes. Each segment is assumed to be generated through a non-parametric mixture process that allows variations of segments within the same scenes to accommodate the dynamic and noisy nature of many real-world surveillance videos. The scene and mixture component assignment of BN-SVP also induces a pairwise similarity among segments, resulting in non-parametric construction of a submodular set function. Integrating this function with an MIL loss effectively exposes the model to a diverse set of potentially positive instances to improve its training. A greedy algorithm is developed to optimize the submodular function and support efficient model training. Our theoretical analysis ensures a strong performance guarantee of the proposed algorithm. The effectiveness of the proposed approach is demonstrated over multiple real-world anomaly video datasets with robust detection performance.
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