Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem. A popular way to tackle this problem is by utilizing an autoencoder (AE) trained only on normal data. At test time, the AE is then expected to reconstruct the normal input well while reconstructing the anomalies poorly. However, several studies show that, even with normal data only training, AEs can often start reconstructing anomalies as well which depletes their anomaly detection performance. To mitigate this, we propose a temporal pseudo anomaly synthesizer that generates fake-anomalies using only normal data. An AE is then trained to maximize the reconstruction loss on pseudo anomalies while minimizing this loss on normal data. This way, the AE is encouraged to produce distinguishable reconstructions for normal and anomalous frames. Extensive experiments and analysis on three challenging video anomaly datasets demonstrate the effectiveness of our approach to improve the basic AEs in achieving superiority against several existing state-of-the-art models.
翻译:由于异常实例有限,视频异常的探测往往被视为单级分类问题。解决这一问题的一种普遍方式是使用仅受过正常数据培训的自动编码器(AE),然后在测试时,AE可望在重建异常情况时很好地重建正常输入,然而,若干研究表明,即使只有正常数据培训,AE往往可以开始重建异常情况,并消耗其异常情况检测性能。为了减轻这一点,我们提议了一种临时假异常合成器,仅使用正常数据产生假异常情况。然后,AE接受培训,最大限度地增加假异常情况重建损失,同时将正常数据损失减少到最小。这样,AE就鼓励AE为正常和异常情况框架进行可辨别重建。对三个具有挑战性的视频异常数据集进行广泛的实验和分析,表明我们改进基本异常情况的方法在取得优于若干现有最先进的模型方面的有效性。