This paper presents a new method for anomaly detection in surveillance videos using deep
learning. The proposed method is based on a deep network trained to identify objects and …

This paper presents a novel deep learning-based approach for anomaly detection in
surveillance films. A deep network that has been trained to recognize objects and human …

This paper describes a method for learning anomaly behavior in the video by finding an
attention region from spatiotemporal information, in contrast to the full-frame learning. In our …

In this paper we propose a deep learning framework to identify anomalous events in
surveillance videos. Anomalous events are those which do not adhere to normal behaviour …

Anomaly detection is finding various anomalous activities taking place in the video. Using an
unsupervised learning technique, surveillance videos identify various real-time video …

Automatic video surveillance is an active research area in recent times to enhance security
features. Based on the crowd behavior, the normal and abnormal scenarios can be detected …

In current technological era, surveillance systems generate an enormous volume of video
data on a daily basis, making its analysis a difficult task for computer vision experts …

In the present technological world, surveillance cameras generate an immense amount of
video data from various sources, making its scrutiny tough for computer vision specialists. It …

Finding strange behavior in busy places is important and a hot topic in the computer vision
and information retrieval communities. Unlike the hand-made features that are often used in …

Automatic anomaly detection in real-world video surveillance is still challenging. In this
paper, we propose an autoencoder architecture based on a stacked convolutional LSTM …