Abandoned Object Detection Using Pixel-Based Finite State Machine and Single Shot Multibox Detector

This paper proposes a robust, scalable framework for automatic detection of abandoned, stationary objects in real time surveillance videos that can pose a security threat. We use the sViBe background modeling method to generate a long-term and a short-term background model to extract foreground objects. Subsequently, a pixel-based FSM detects stationary candidate objects based on the temporal transition of code patterns. In order to classify the stationary candidate objects, we use deep learning method (SSD: Single Shot MultiBox Detector) to detect person and some suspected type of objects which include backpack, handbag. In order to suppress any false alarm, we remove other stationary candidate objects other than the suspected stationary objects. After stationary object detection, we also check if there is no person near by the suspected detected objects for a particular time. We tested the system on four standard public datasets. The results show that our method outperforms the performance of existing results while also being robust to temporary occlusions and illumination changes.