  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.
