Serve receive-to-attack (SR2A) is the most principal way to gain points in volleyball games. In addition, the positions of players on the court reveal informative clues about both offensive and defensive formations. Hence, in this paper, we propose an effective system capable of extracting SR2A periods from long broadcast volleyball videos (taken from a pan-tilt-zoom camera), and then locating the players using a novel histogram-based approach. Enriched visual presentation can be provided to give the audience or professional players/coaches a further sight into the game. The proposed system consists of four major processing modules, including court detection, SR2A period extraction, camera calibration, and histogram-based player localization. The experiments conducted on broadcast volleyball videos show promising results.
This study proposes a method for designing and calibrating a millimeter-wave (mm-wave) multiple-input multiple-output (MIMO) antenna module. Herein, we adopt a design example involving a 64-element MIMO antenna array arranged in a triangular lattice (instead of the commonly used rectangular lattice) to achieve a 3°dB enhancement in effective isotropic radiated power. Analyzing a grating lobe diagram indicates a scan volume of ±60°/±45° in the azimuth/elevation direction. To calibrate the massive mm-wave MIMO antenna module, we propose a modified genetic algorithm to align the amplitude/phase of the transmitting/receiving signal of the module to reduce the time required for the calibration process. Finally, we conducted a simple experiment to validate the proposed method.
Advanced vehicle safety is a recently emerging issue, appealed from the rapidly explosive population of car owners. Increasing driver assistance systems have been designed for warning drivers of what should be noticed by analyzing surrounding environments with sensors and/or cameras. As one of the hazard road conditions, road bumps not only damage vehicles but also cause serious danger, especially at night or under poor lighting conditions. In this paper we propose a vision-based road bump detection system using a front-mounted car camcorder, which tends to be widespread deployed. First, the input video is transformed into a time-sliced image, which is a condensed video representation. Consequently, we estimate the vertical motion of the vehicle based on the time-sliced image and infer the existence of road bumps. Once a bump is detected, the location fix obtained from GPS is reported to a central server, so that the other vehicles can receive warnings when approaching the detected bumpy regions. Encouraging experimental results show that the proposed system can detect road bumps efficiently and effectively. It can be expected that traffic security will be significantly promoted through the mutually beneficial mechanism that a driver who is willing to report the bumps he/she meets can receive warnings issued from others as well.
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