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.

One of the most dominant factors in developing tactile modules is the ability to generate abundant vibrotactile sensation. This paper presents a new vibrotactile module which can stimulate two mechanoreceptors at the same time without any mechanical vibration motors. To realize that, we first design an electro-tactile beat module (an ETB module) consisting of a lower part, a connection part and an upper part. The two electrodes were designed in an interdigitated pattern and were applied to the upper part. By applying two voltage inputs with slightly different frequencies to two electrodes in the proposed ETB module, respectively, we can create beat-patterned vibration. Furthermore, we can create normal vibration with the proposed ETB module by applying same frequency to the two electrodes. Experiments were conducted to validate the haptic performance of the proposed prototype. The results show that the proposed ETB module can create not only beat-patterned vibration but also normal vibration. The results also show that it can generate strong enough vibration to stimulate mechanoreceptors in wide frequency ranges.

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.

Normally, to extract the defect in TFT-LCD inspection system, the image is obtained by using line scan camera or area scan camera which is achieved by CCD or CMOS sensor. Because of the limited dynamic range of CCD or CMOS sensor as well as the effect of the illumination, these images are frequently degraded and the important features are hard to decern by a human viewer. In order to overcome this problem, the feature vectors in the image are obtained by using the average intensity difference between defect and background based on the weber's law and the standard deviation of the background region. The defect detection method uses non-linear SVM (Supports Vector Machine) method using the extracted feature vectors. The experiment results show that the proposed method yields better performance of defect classification methods over conveniently method.

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