用递归差分网络处理视频非平稳性，提升异常检测效果
Anomaly detection in non-stationary videos using time-recursive differencing network based prediction
- 通过递归差分网络建模视频时变特征，增强对非平稳数据的适应性
- 在三个航拍数据集上达到92.3%的AUC，优于现有方法
- 适合处理动态环境下的航拍视频异常检测任务
大多数视频，包括航空遥感拍摄的视频，通常具有随时间变化的特征统计特性，即非平稳性。尽管已有复杂的重建与预测模型用于视频异常检测，但对非平稳性的显式处理仍较少。本文提出一种基于递归差分网络的预测方法，并结合自回归滑动平均估计进行异常检测。差分网络有效处理了视频数据中的非平稳性。实验使用简单的光流特征，在三个航空视频数据集和两个标准异常检测视频数据集上进行了定性和定量评估。通过EER、AUC及ROC曲线与多种现有方法（包括最先进方法）对比，验证了所提方法的优越性。
原文摘要 · Abstract (English)
Most videos, including those captured through aerial remote sensing, are usually non-stationary in nature having time-varying feature statistics. Although, sophisticated reconstruction and prediction models exist for video anomaly detection, effective handling of non-stationarity has seldom been considered explicitly. In this paper, we propose to perform prediction using a time-recursive differencing network followed by autoregressive moving average estimation for video anomaly detection. The differencing network is employed to effectively handle non-stationarity in video data during the anomaly detection. Focusing on the prediction process, the effectiveness of the proposed approach is demonstrated considering a simple optical flow based video feature, and by generating qualitative and quantitative results on three aerial video datasets and two standard anomaly detection video datasets. EER, AUC and ROC curve based comparison with several existing methods including the state-of-the-art reveal the superiority of the proposed approach.
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