Due to the increase in surveillance systems, there is a massive increase in surveillance
data. As of now, the key challenge for video surveillance systems is analyzing these large …

Rare event detection (RED) involves the identification and detection of events characterized
by low frequency of occurrences, but of high importance or impact. This paper presents a …

Crowd anomaly detection is one of the most popular topics in computer vision in the context
of smart cities. A plethora of deep learning methods have been proposed that generally …

Zero-Shot Learning (ZSL) is related to training machine learning models capable of
classifying or predicting classes (labels) that are not involved in the training set (unseen …

The uncontrolled outburst in population has led to crowd gatherings in various public places
causing panic and disaster in certain unpleasant and extreme conditions. A study on the …

In potential disasters, real world scenarios and public events, understanding of crowd
psychology is a challenging process and detection of crowd behaviors in those events is …

Convolution neural networks (CNNs) are used to perform the image recognition and other
visual system job. Their implementation in the detection systems of anomaly will essentially …

The increasing number of people is a major cause of disasters that occur due to
overcrowding. The gatherings of crowds in public places are a source of panic, which results …

The increasing number of people is a major cause of disasters that occur due to
overcrowding. The gatherings of crowds in public places are a source of panic, which results …

This thesis tackles the inefficiencies associated with manual annotation in soccer event
detection, a process that is time-consuming, expensive, and difficult to scale during major …