Department of Computer Science, Yonsei University, 50 Yonsei-ro, Sudaemoon-gu, Seoul 03722, South Korea.
Int J Neural Syst. 2020 Jun;30(6):2050034. doi: 10.1142/S0129065720500343. Epub 2020 May 28.
As the surveillance devices proliferate, various machine learning approaches for video anomaly detection have been attempted. We propose a hybrid deep learning model composed of a video feature extractor trained by generative adversarial network with deficient anomaly data and an anomaly detector boosted by transferring the extractor. Experiments with UCSD pedestrian dataset show that it achieves 94.4% recall and 86.4% precision, which is the competitive performance in video anomaly detection.
随着监控设备的普及，人们尝试了各种用于视频异常检测的机器学习方法。我们提出了一种混合深度学习模型，该模型由一个通过生成对抗网络训练的视频特征提取器和一个通过迁移该提取器增强的异常检测器组成。在 UCSD 行人数据集上的实验表明，该模型的召回率达到了 94.4%，准确率达到了 86.4%，在视频异常检测方面具有竞争力。
