Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection

We present a block-wise approach to detect stationary objects based on spatio-temporal change detection. First, block candidates are extracted by filtering out consecutive blocks containing moving objects. Then, an online clustering approach groups similar blocks at each spatial location over time via statistical variation of pixel ratios. The stability changes are identified by analyzing the relationships between the most repeated clusters at regular sampling instants. Finally, stationary objects are detected as those stability changes that exceed an alarm time and have not been visualized before. Unlike previous approaches making use of Background Subtraction, the proposed approach does not require foreground segmentation and provides robustness to illumination changes, crowds and intermittent object motion. The experiments over an heterogeneous dataset demonstrate the ability of the proposed approach for short-and long-term operation while overcoming challenging issues.