Identifying unusual crowd events is highly challenging, laborious, and prone to errors in
video surveillance applications. We propose a novel end-to-end deep learning architecture …

Abnormal event detection is one of the most challenging tasks in computer vision. Many
existing deep anomaly detection models are based on reconstruction errors, where the …

In this paper, we present a hybrid deep network based approach for crowd anomaly
detection in videos. For improved performance, the proposed approach exploits deep and …

Abnormal event detection in video surveillance is extremely important, especially for
crowded scenes. In recent years, many algorithms have been proposed based on hand …

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 …

Anomaly detection in crowded scenes plays a crucial role in automatic video surveillance to
avert any casualty in the areas witnessing the high amount of footfalls. The key challenge for …

Anomalous event detection in videos is an important and challenging task. This paper
proposes a deep representation approach to the problem, which extracts and represents …

Recently, due to its widespread applications in public safety, anomaly detection in crowd
scenes has become a hot topic. Some deep-learning-based methods attain significant …

This study proposed an AlexNet-based crowd anomaly detection model in the video (image
frames). The proposed model was comprised of four convolution layers (CLs) and three …

Anomaly event detection in crowd scenes is extremely important; however, the majority of
existing studies merely use hand‐crafted features to detect anomalies. In this study, a novel …