论文标题
使用GAN和转移学习的城市监视视频中的异常事件检测
Abnormal Event Detection in Urban Surveillance Videos Using GAN and Transfer Learning
论文作者
论文摘要
城市监视视频中的异常事件检测（AED）有多个挑战。与其他计算机视觉问题不同，AED不仅取决于框架的内容。这也取决于对象的外观及其在场景中的动作。已经提出了各种方法来解决AED问题。其中，基于深度学习的方法显示了最佳结果。本文基于深度学习方法，并提供了一种通过处理时空数据来检测和定位视频中异常事件的有效方法。本文使用生成的对抗网络（GAN），并在训练有素的卷积神经网络（CNN）上执行转移学习算法，从而导致准确有效的模型。通过处理视频的光流信息，进一步提高了模型的效率。本文在两个基准数据集上运行实验，以解决AED问题（UCSD PEDS1和UCSD PEDS2），并将结果与其他先前方法进行比较。比较基于各种标准，例如曲线下的面积（AUC）和真实的正率（TPR）。实验结果表明，所提出的方法可以有效地检测和定位人群场景中的异常事件。
Abnormal event detection (AED) in urban surveillance videos has multiple challenges. Unlike other computer vision problems, the AED is not solely dependent on the content of frames. It also depends on the appearance of the objects and their movements in the scene. Various methods have been proposed to address the AED problem. Among those, deep learning based methods show the best results. This paper is based on deep learning methods and provides an effective way to detect and locate abnormal events in videos by handling spatio temporal data. This paper uses generative adversarial networks (GANs) and performs transfer learning algorithms on pre trained convolutional neural network (CNN) which result in an accurate and efficient model. The efficiency of the model is further improved by processing the optical flow information of the video. This paper runs experiments on two benchmark datasets for AED problem (UCSD Peds1 and UCSD Peds2) and compares the results with other previous methods. The comparisons are based on various criteria such as area under curve (AUC) and true positive rate (TPR). Experimental results show that the proposed method can effectively detect and locate abnormal events in crowd scenes.
