Stationary Object Detection for Vision-Based Smart Monitoring System

Abstract

This work proposes a method for detecting stationary objects in a vision-based smart monitoring system. A stationary object is defined as an object that previously moves, but currently remains stable in a certain location. The case samples for such objects include abandoned objects and illegally parked vehicles, whose surveillance is a crucial task for ensuring public safety and security. The proposed method is based on dual background modeling for separating foreground from background. To extract the candidates of stationary objects, a Gaussian mixture model-based cumulative dual foreground difference is implemented. An SVM-based object classifier is then integrated to verify the region candidates whether they are vehicle, human, or other objects. If the object is classified as either vehicle or baggage, the duration of the object being stable is counted using detection-based tracking. If the duration exceeds a certain value, an alarm will be triggered. In experiment, the method is evaluated using public datasets iLIDS and ISLab.