Two-stage unattended object detection method with proposals

Unattended object detection is a crucial task in visual surveillance systems. However, it is challenging in handling false alarms and miss detection rate. In this paper, a two-stage method for the unattended object detection is proposed where the first stage tries to detect all possible unattended objects and prevent miss detections by considering attributes of objects such as staticness, foregroundness, and abandonment. This stage is called the unattended object proposal stage. In the second stage, our method reduces false alarms with candidates obtaining from the first stage by using a deep learning similarity matching between candidates and the background model. With the capability of reducing false alarms and miss detections, our method can be applied in large-scale deployment systems for unattended object detection.