Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

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 …

Human activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Frame reconstruction (current or future frames) based on Auto-Encoder (AE) is a popular
method for video anomaly detection. With models trained on the normal data, the …

Novelty detection is the process of identifying the observation (s) that differ in some respect
from the training observations (the target class). In reality, the novelty class is often absent …

Reliably modeling normality and differentiating abnormal appearances from normal cases is
a very appealing approach for detecting pathologies in medical images. A plethora of such …

With the recent advances in deep neural networks, anomaly detection in multimedia has
received much attention in the computer vision community. While reconstruction-based …

Abnormal event detection in the surveillance video is an essential but challenging task, and
many methods have been proposed to deal with this problem. The previous methods either …

A popular method for anomaly detection is to use the generator of an adversarial network to
formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of …