Motion detection algorithms that can be applied to surveillance cameras such as CCTV (Closed Circuit Television) have been studied extensively. Motion detection algorithm is mostly based on background subtraction. One main issue in this technique is that false positives of dynamic backgrounds such as wind shaking trees and flowing rivers might occur. In this paper, we proposed a method to search for dynamic background region by analyzing the video and removing false positives by re-checking false positives. The proposed method was evaluated based on CDnet 2012/2014 dataset obtained at "changedetection.net" site. We also compared its processing speed with other algorithms.

The eye blink rate, a major human physiological response, directly affects ocular diseases, such as keratitis and dry eye syndrome. It has been shown that the eye blink rate in normal eyes has a certain frequency for individuals, from 6–30 times/min. It was suggested in a previous study that the eye blink rate can be decreased during the viewing of high-intensity and realistic content. Therefore, in this paper, we examine the change of the eye blink rate during the HMD (head-mounted display) viewing of VR (virtual reality) contents; accordingly, we propose an algorithm to measure the eye blink rate as well as compare and analyze this rate in three different environments (natural, monitor, and HMD). We confirmed that IPD (interpupillary distance) and phoria affected the eye blink rate in each environment. In this experiment, 21 subjects (28.38 ± 6.87 years) were selected, and a paired t-test was performed for changes in the eye blink rate over 1 min for each environment. The IPD and phoria effects on the eye blink rate were confirmed using the Spearman’s correlation coefficient. In this experiment, the eye blink rate was decreased in the monitor and HMD environments compared with the natural environment, while that in the HMD environment was decreased compared with the monitor environment. The results of the correlation analysis of far IPD and the eye blink rate show no statistical significance or correlation. The correlation analysis of near IPD and the eye blink rate showed a strong positive correlation of the eye blink rate in the monitor environment. The correlation analysis of distance phoria and the eye blink rate showed a strong negative correlation of the eye blink rate in the HMD environment. The correlation analysis of near-field phoria and the eye blink rate showed a strong negative correlation of the eye blink rate in the HMD environment. It is expected that the results of this study will be used as a VR-viewing recommendation.

Image-based three-dimensional (3D) reconstruction is a process of extracting 3D information from an object or entire scene while using low-cost vision sensors. A structure-from-motion coupled with multi-view stereo (SFM-MVS) pipeline is a widely used technique that allows 3D reconstruction from a collection of unordered images. The SFM-MVS pipeline typically comprises different processing steps, including feature extraction and feature matching, which provide the basis for automatic 3D reconstruction. However, surfaces with poor visual texture (repetitive, monotone, etc.) challenge the feature extraction and matching stage and affect the quality of reconstruction. The projection of image patterns while using a video projector during the image acquisition process is a well-known technique that has been shown to be successful for such surfaces. In this study, we evaluate the performance of different feature extraction methods on texture-less surfaces with the application of synthetically generated noise patterns (images). Seven state-of-the-art feature extraction methods (HARRIS, Shi-Tomasi, MSER, SIFT, SURF, KAZE, and BRISK) are evaluated on problematic surfaces in two experimental phases. In the first phase, the 3D reconstruction of real and virtual planar surfaces evaluates image patterns while using all feature extraction methods, where the patterns with uniform histograms have the most suitable morphological features. The best performing pattern from Phase One is used in Phase Two experiments in order to recreate a polygonal model of a 3D printed object using all of the feature extraction methods. The KAZE algorithm achieved the lowest standard deviation and mean distance values of 0.0635 mm and −0.00921 mm, respectively.

The use of human gesturing to interact with devices such as computers or smartphones has presented several problems. This form of interaction relies on gesture interaction technology such as Leap Motion from Leap Motion, Inc, which enables humans to use hand gestures to interact with a computer. The technology has excellent hand detection performance, and even allows simple games to be played using gestures. Another example is the contactless use of a smartphone to take a photograph by simply folding and opening the palm. Research on interaction with other devices via hand gestures is in progress. Similarly, studies on the creation of a hologram display from objects that actually exist are also underway. We propose a hand gesture recognition system that can control the Tabletop holographic display based on an actual object. The depth image obtained using the latest Time-of-Flight based depth camera Azure Kinect is used to obtain information about the hand and hand joints by using the deep-learning model CrossInfoNet. Using this information, we developed a real time system that defines and recognizes gestures indicating left, right, up, and down basic rotation, and zoom in, zoom out, and continuous rotation to the left and right.

Recently, holographic display and computer-generated holograms calculated from real existing objects have been more actively investigated to support holographic video applications. In this paper, we proposed a method of generating 360-degree color holograms of real 3D objects in an efficient manner. 360-degree 3D images are generated using the actual 3D image acquisition system consisting of a depth camera and a turntable and intermediate view generation. Then, 360-degree color holograms are calculated using a viewing-window-based computer-generated hologram. We confirmed that floating 3D objects are faithfully reconstructed around a 360-degree direction using our 360-degree tabletop color holographic display.