Dual background models have been widely used for detecting stationary objects in video surveillance systems. However, there is a problem that both abandoned and stolen objects are equally detected as stationary objects, making it difficult to distinguish them. Another problem is the ghost region created by shadow shift or light changes, which makes the discrimination issue more complicated. In this paper, we present an efficient method to distinguish abandoned objects, stolen objects, and ghost regions in the surveillance video. This method contains two main strategies: the first one is the dual background model for extracting candidate stationary objects, the second one is object segmentation based on mask regions with CNN features (Mask R-CNN) for providing the object mask information. The basic idea is: given a candidate stationary object from the background model, it is checked whether a corresponding segmented object exists in the current video frame or the previous background frame to take into account the current and past situations. And the final state of the candidate stationary object is determined by considering various situations through the comparative analysis technique presented in this paper. The proposed algorithm has qualitatively experimented with our own dataset focusing on the discrimination issue, which generated satisfactory results. Therefore, it is expected to be widely applied to automatic detection of stolen objects as well as abandoned objects in open environments such as exhibition halls and public parks where existing intrusion detection-based security services are difficult to be deployed. INDEX TERMS Abandoned object detection, stolen object detection, ghost region, dual background model, mask R-CNN.
Most existing abandoned object detection algorithms use foreground information generated from background models. Detection using the background subtraction technique performs well under normal circumstances. However, it has a significant problem where the foreground information is gradually absorbed into the background as time passes and disappears, making it very vulnerable to sudden illumination changes that increase the false alarm rate. This paper presents an algorithm for detecting abandoned objects using a dual background model, which is robust even in illumination changes as well as other complex circumstances like occlusion, long-term abandonment, and owner re-attendance. The proposed algorithm can adapt quickly to various illumination changes. And also, it can precisely track the target objects to determine whether it is abandoned regardless of the existence of foreground information and the effect from the illumination changes, thanks to the largest-contour-based presence authentication mechanism proposed in this paper. For performance evaluation, we trialed the algorithm with the PETS2006, ABODA datasets as well as our dataset, especially to demonstrate its robustness in various illumination changes.
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