Real time anomalies detection on video
This work addresses the need for proactive detection rather than retrospective review in security camera systems, though it appears incremental as it applies established methods to this domain.
The authors tackled the problem of real-time anomaly detection in video surveillance by proposing a deep learning approach combining CNNs for feature extraction and LSTMs/GRUs for time-series analysis, but no concrete results or numbers are provided.
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.