Nobody likes Mondays: foreground detection and behavioral patterns analysis in complex urban scenes
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
| Hi-index | 0.00 |
We introduce the Longterm Observation of Scenes (with Tracks) dataset. This dataset comprises videos taken from streaming outdoor webcams, capturing the same half hour, each day, for over a year. LOST contains rich metadata, including geolocation, day-by-day weather annotation, object detections, and tracking results. We believe that sharing this dataset opens opportunities for computer vision research involving very long-term outdoor surveillance, robust anomaly detection, and scene analysis methods based on trajectories. Efficient analysis of changes in behavior in a scene at very long time scale requires features that summarize large amounts of trajectory data in an economical way. We describe a trajectory clustering algorithm and aggregate statistics about these exemplars through time and show that these statistics exhibit strong correlations with external meta-data, such as weather signals and day of the week.