Hybrid Architecture for Real-Time Video Anomaly Detection: Integrating Spatial and Temporal Analysis
This work addresses video anomaly detection for surveillance or security applications, but it appears incremental as it combines existing methods without introducing a fundamentally new approach.
The paper tackles real-time anomaly detection in video data by proposing a hybrid architecture that integrates spatial and temporal analyses, using models like VGG19+GRU and YOLOv7, and compares parallel and serial configurations to evaluate effectiveness.
In this paper, we propose a new architecture for real-time anomaly detection in video data, inspired by human behavior combining spatial and temporal analyses. This approach uses two distinct models: (i) for temporal analysis, a recurrent convolutional network (CNN + RNN) is employed, associating VGG19 and a GRU to process video sequences; (ii) regarding spatial analysis, it is performed using YOLOv7 to analyze individual images. These two analyses can be carried out either in parallel, with a final prediction that combines the results of both analysis, or in series, where the spatial analysis enriches the data before the temporal analysis. Some experimentations are been made to compare these two architectural configurations with each other, and evaluate the effectiveness of our hybrid approach in video anomaly detection.