Abstract Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN)
algorithm known for its ability to effectively analyze and process sequential data with long …

Video surveillance has shown encouraging outcomes to monitor human activities and
prevent crimes in real time. To this extent, violence detection (VD) has received substantial …

Surveillance videos are crucial for crime prevention and public safety, yet the challenge of
defining abnormal events hinders their effectiveness, limiting the applicability of supervised …

The identification of anomalies in videos is a particularly complex visual challenge, given the
wide variety of potential real-world events. To address this issue, our paper introduces a …

Object detection using deep learning has attracted considerable interest from researchers
because of its competency in performing state-of-the-art tasks, including detection …

The world's expanding populace, the variety of human social factors, and the densely
populated environment make humans feel uncertain. Individuals need a safety officer who …

Abnormal behavior has become the leading cause of injuries among the elderly in the
modern society. Elderly anomaly is a widespread concern in both academic and industrial …

In videos, anomaly detection is challenging due to its diverse nature in different application
domains. Reconstruction and prediction-based methods have been widely employed to …

Reliable knowledge of driving states is of great importance to ensure road safety. Anomaly
detection in driving behavior means recognizing anomalous driving states as a direct result …

Abstract Multiple Instance Learning (MIL) is widely used to locate abnormal video frames in
surveillance videos due to its ability to work with weakly-labeled data. On the other hand …