Real time anomalies detection on video

TL;DR

This paper proposes a deep learning method combining CNNs and LSTM/GRU models for real-time anomaly detection in video streams to improve security incident response.

Contribution

It introduces a novel deep learning framework integrating convolutional and sequential models for real-time video anomaly detection.

Findings

Effective extraction of video features using CNNs

Sequential models identify anomalies in real-time

Potential for improved security monitoring

Abstract

Nowadays, many places use security cameras. Unfortunately, when an incident occurs, these technologies are used to show past events. So it can be considered as a deterrence tool than a detection tool. In this article, we will propose a deep learning approach trying to solve this problematic. This approach uses convolutional models (CNN) to extract relevant characteristics linked to the video images, theses characteristics will form times series to be analyzed by LSTM / GRU models.