Recent video anomaly detection methods focus on reconstructing or predicting frames. Under this umbrella, the long-standing inter-class data-imbalance problem resorts to the imbalance between foreground and stationary background objects in video anomaly detection and this has been less investigated by existing solutions. Naively optimizing the reconstructing loss yields a biased optimization towards background reconstruction rather than the objects of interest in the foreground. To solve this, we proposed a simple yet effective solution, termed attention-driven loss to alleviate the foreground-background imbalance problem in anomaly detection. Specifically, we compute a single mask map that summarizes the frame evolution of moving foreground regions and suppresses the background in the training video clips. After that, we construct an attention map through the combination of the mask map and background to give different weights to the foreground and background region respectively. The proposed attention-driven loss is independent of backbone networks and can be easily augmented in most existing anomaly detection models. Augmented with attention-driven loss, the model is able to achieve AUC 86.0% on Avenue, 83.9% on Ped1, 96% on Ped2 datasets. Extensive experimental results and ablation studies further validate the effectiveness of our model.

中文翻译：

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视频监控中异常检测的注意力驱动损失

最近的视频异常检测方法侧重于重建或预测帧。在此保护伞下，长期存在的类间数据不平衡问题诉诸于视频异常检测中前景和静止背景对象之间的不平衡，而现有解决方案对此的研究较少。天真地优化重建损失会产生对背景重建而不是前景中感兴趣的对象的有偏见的优化。为了解决这个问题，我们提出了一个简单而有效的解决方案，称为注意力驱动损失，以缓解异常检测中的前景-背景不平衡问题。具体来说，我们计算了一个单一的掩码图，它总结了移动前景区域的帧演变并抑制了训练视频剪辑中的背景。之后，我们通过蒙版图和背景的组合构建注意力图，分别为前景和背景区域赋予不同的权重。所提出的注意力驱动损失独立于骨干网络，并且可以在大多数现有的异常检测模型中轻松增加。增加了注意力驱动的损失，该模型能够在 Avenue 上达到 86.0%，在 Ped1 上达到 83.9%，在 Ped2 数据集上达到 96%。广泛的实验结果和消融研究进一步验证了我们模型的有效性。该模型能够在 Avenue 上达到 86.0%、在 Ped1 上达到 83.9%、在 Ped2 数据集上达到 96%。广泛的实验结果和消融研究进一步验证了我们模型的有效性。该模型能够在 Avenue 上达到 86.0%、在 Ped1 上达到 83.9%、在 Ped2 数据集上达到 96%。广泛的实验结果和消融研究进一步验证了我们模型的有效性。