The movements of the human body are difficult to capture owing to the complexity of the three‐dimensional skeleton model and occlusion problems. In this paper, we propose a motion capture system that tracks dynamic human motions in real time. Without using external markers, the proposed system adopts multiple depth sensors (Microsoft Kinect) to overcome the occlusion and body rotation problems. To combine the joint data retrieved from the multiple sensors, our calibration process samples a point cloud from depth images and unifies the coordinate systems in point clouds into a single coordinate system via the iterative closest point method. Using noisy skeletal data from sensors, a posture reconstruction method is introduced to estimate the optimal joint positions for consistent motion generation. Based on the high tracking accuracy of the proposed system, we demonstrate that our system is applicable to various motion‐based training programs in dance and Taekwondo.
As various unmanned autonomous driving technologies such as autonomous vehicles and autonomous driving drones are being developed, research on FMCW radar, a sensor related to these technologies, is actively being conducted. The range resolution, which is a parameter for accurately detecting an object in the FMCW radar system, depends on the modulation bandwidth. Expensive radars have a large modulation bandwidth, use the band above 77 GHz, and are mainly used as in-vehicle radar sensors. However, these high-performance radars have the disadvantage of being expensive and burdensome for use in areas that require precise sensors, such as indoor environment motion detection and autonomous drones. In this paper, the range resolution is improved beyond the limited modulation bandwidth by extending the beat frequency signal in the time domain through the proposed Adaptive Mirror Padding and Phase Correction Padding. The proposed algorithm has similar performance in the existing Zero Padding, Mirror Padding, and Range RMSE, but improved results were confirmed through the ρs indicating the size of the side lobe compared to the main lobe and the accurate detection rate of the OS CFAR. In the case of ρs, it was confirmed that with single targets, Adaptive Mirror Padding was improved by about 3 times and Phase Correct Padding was improved by about 6 times compared to the existing algorithm. The results of the OS CFAR were divided into single targets and multiple targets to confirm the performance. In single targets, Adaptive Mirror Padding improved by about 10% and Phase Correct Padding by about 20% compared to the existing algorithm. In multiple targets, Phase Correct Padding improved by about 20% compared to the existing algorithm. The proposed algorithm was verified through the MATLAB Tool and the actual FMCW radar. As the results were similar in the two experimental environments, it was verified that the algorithm works in real radar as well.
Abstract-We present a new method for comparing users' motions captured in real time by multiple Kinect sensors with an expert's movements stored in a DB so as to help users experience and learn how to dance. Recently, lots of experience games where users copy some motions to score have been developed. However, it is difficult to collect users' joint data or to clearly compare movements with one sensor because of blocked body parts and unsuccessful tracking. Therefore, such games cannot be applicable to learning beyond simple in-game experiences. As an alternative, this study proposes a method for using multiple Kinect sensors to combine skeletal data and thus to retrieve motions. Also, we propose a method for directly comparing postures between characters of different body sizes. The direct comparison of postures ensures accuracy as no motion is adapted in the process. The proposed dance experience learning system is suitable for learning how to dance as it is easy to implement, features real-time posture comparison and displays the results of important body parts compared.
This paper suggests a new flight model for a realistic golf game simulation. The forces at work in a ball during flight include lift, drag, and gravity. A golf ball flying at a high spinning speed has a different lift and drag according to its spin rate and Reynolds number. Also, the drag force of a ball is largely different according to the size, depth, and number of dimples in the ball. However, these differences are not reflected in golf game simulations. This paper suggests a method for changing the simulated flight distance of a golf ball in accordance with its drag based on changes in Reynolds number and dimple characteristics. Also, since the flight distance of a ball changes in real world depending on the temperature, humidity, and altitude, these changes should be reflected in realistic games as well. When the temperature, humidity, and altitude are given, the density and pressure of air in the virtual environment are calculated, and through these calculated values, the flight distance of the ball can be changed. Finally, the effect of wind should be considered. Usually, in games, the wind effect is handled using a constant term. However, the strength of a real wind changes following its height. By applying a function reflecting this change, the strength of the wind is reflected differently according to the elevation of the ball. Through the method suggested in this paper, we can calculate a realistic flight trajectory for a simulated golf ball.
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