Lost in Time: Temporal Analytics for Long-Term Video Surveillance
This work addresses the need for temporal analytics in video surveillance to understand human behavior patterns, but it is incremental as it builds on existing object detection and tracking methods.
The authors tackled the problem of analyzing long-term video surveillance data by proposing descriptive and predictive analytics schemes, resulting in heatmap and footmap visualizations for trajectory patterns and anomaly prediction methods that demonstrated reasonable performance on one year of data from a single camera.
Video surveillance is a well researched area of study with substantial work done in the aspects of object detection, tracking and behavior analysis. With the abundance of video data captured over a long period of time, we can understand patterns in human behavior and scene dynamics through data-driven temporal analytics. In this work, we propose two schemes to perform descriptive and predictive analytics on long-term video surveillance data. We generate heatmap and footmap visualizations to describe spatially pooled trajectory patterns with respect to time and location. We also present two approaches for anomaly prediction at the day-level granularity: a trajectory-based statistical approach, and a time-series based approach. Experimentation with one year data from a single camera demonstrates the ability to uncover interesting insights about the scene and to predict anomalies reasonably well.