Identifying Loitering Behavior with Trajectory Analysis
Johnny Núñez, Zenjie Li, Sérgio Escalera, Kamal Nasrollahi · 2024
The act of remaining in a public area for an extended period is commonly referred to as Loitering, and it is often viewed as suspicious activity with regard to public safety. The research landscape on loitering detection is diverse, featuring various definitions and methodologies. This lack of standardization in defining loitering hamper the general-izability of detection methods. Our work, focuses on providing a clear definition of loitering and detecting it through trajectory analysis. We enrich the field of loitering detection research by introducing a dataset with annotated loitering behaviors. Our contribution is to annotate loi- tering behavior in the Long-term Thermal Drift Dataset, which already complies with privacy standards. The dataset features a variety of loitering behaviors observed through a real-world thermal surveillance camera across different environmental scenarios. To identify loitering behavior, we employ trajectory analysis methods. These methods quantify parameters such as movement directionality, pace, and dwell time, providing fundamental aspects for loitering detection studies. The dataset and the code are avail- able on https://github.com/johnnynunez/RS-WACV24_Loitering.