Enhancing Anomaly Detection in Videos using a Combined YOLO and a VGG GRU Approach

• Main Authors: Poirier, Fabien, Jaziri, Rakia, Srour, Camille, Bernard, Gilles
• Format: Conference Proceeding
• Language: English
• Subjects: Analytical models; Anomaly detection; Computer architecture; Data models; GRU; Task analysis; Temporal Analysis; VGG19; Video sequences; Videos; Visualization; YOLO; YOLOv7
• ISSN: 2161-5330
• DOI: 10.1109/AICCSA59173.2023.10479307

In this paper, we propose an innovative architecture for anomaly detection in videos, motivated by the need to answer quickly to danger in monitoring streams, without requiring expensive computational power. Drawing inspiration from human behavior our approach integrates spatial and temporal analyses. For the temporal analysis, which classifies video sequences, we associate a recurrent convolutional network combining Visual Geometry Group Net 19 (VGG19) and Gated Reccurrent Units (GRU), with a Multilayer Perceptron (MLP). Simultaneously, the spatial analysis of individual images is conducted through You Only Look Once version 7 (YOLOv7). Then, both predictions are combined to perform the final prediction, where an anomaly is signaled if a perceived suspicious object or unexpected action occurs on the screen. Our experimental results shows the integration of both approaches reduces the rate of false negatives, leading to improved identification of anomalous events within video streams for both binary and multi-class models. We also show that multi-class models are less suited for this task than binary models.