Video surveillance systems are popular and used in public places such as market places,
shop** malls, hospitals, banks, streets, education institutions, city administrative offices …

Videos represent the primary source of information for surveillance applications. Video
material is often available in large quantities but in most cases it contains little or no …

Anomaly detection is commonly pursued as a one-class classification problem, where
models can only learn from normal training samples, while being evaluated on both normal …

We address the problem of anomaly detection, that is, detecting anomalous events in a
video sequence. Anomaly detection methods based on convolutional neural networks …

Anomaly detection in video is a challenging computer vision problem. Due to the lack of
anomalous events at training time, anomaly detection requires the design of learning …

In this paper, we propose HF2-VAD, a Hybrid framework that integrates Flow reconstruction
and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the …

Deep autoencoder has been extensively used for anomaly detection. Training on the normal
data, the autoencoder is expected to produce higher reconstruction error for the abnormal …

Anomaly detection in videos remains a challenging task due to the ambiguous definition of
anomaly and the complexity of visual scenes from real video data. Different from the …

Video anomaly detection (VAD) is a significant computer vision problem. Existing deep
neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or …

Anomaly detection in surveillance videos is currently a challenge because of the diversity of
possible events. We propose a deep convolutional neural network (CNN) that addresses …