Anomaly Detection in Surveillance Videos through Object-Oriented Analysis - Auni Khadijah; Abid Dilnawaz (International Journal of Smart Systems, 2023)
Detecting and pinpointing irregularities in surveillance videos has remained a persistent challenge. The current approaches, which are based on patches or trajectories, do not have a semantic understanding of the scenes and may split the targets into fragments. To address this issue, this research proposes a new and efficient algorithm that combines deep object detection and tracking, while fully leveraging spatial and temporal information. A dynamic image is introduced by integrating both appearance and motion information and then fed into an object detection network, which accurately detects and classifies objects, even in crowded and poorly lit scenes. Based on the detected objects, an effective and scale-insensitive feature, named Histogram Variance of Optical Flow Angle (HVOFA), is developed together with motion energy to identify abnormal motion patterns. To further detect missing anomalies and reduce false detections, a post-processing step is carried out with abnormal object tracking. This algorithm outperforms existing methods on established benchmarks.