Browsing by Author "Chu, Hung-Kuo"

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Court Reconstruction for Camera Calibration in Broadcast Basketball Videos
(2016-05)
Wen, Pei-Chih
;
Cheng, Wei-Chih
;
Wang, Yu-Shuen
;
Chu, Hung-Kuo
;
Tang, Nick C.
We introduce a technique of calibrating camera motions in basketball videos. Our method particularly transforms player positions to standard basketball court coordinates and enables applications such as tactical analysis and semantic basketball video retrieval. To achieve a robust calibration, we reconstruct the panoramic basketball court from a video, followed by warping the panoramic court to a standard one. As opposed to previous approaches, which individually detect the court lines and corners of each video frame, our technique considers all video frames simultaneously to achieve calibration; hence, it is robust to illumination changes and player occlusions. To demonstrate the feasibility of our technique, we present a stroke-based system that allows users to retrieve basketball videos. Our system tracks player trajectories from broadcast basketball videos. It then rectifies the trajectories to a standard basketball court by using our camera calibration method. Consequently, users can apply stroke queries to indicate how the players move in gameplay during retrieval. The main advantage of this interface is an explicit query of basketball videos so that unwanted outcomes can be prevented. We show the results in Figs. 1, 7, 9, 10 and our accompanying video to exhibit the feasibility of our technique.
Motion Manipulation for Characters in Videos
(2015)
Chu, Hung-Kuo
;
Yeh, Che-Hua
;
Tang, Nick C.
;
Tyan, Hsiao-Rong
;
Wang, Yu-Shuen
Multimedia content creation and manipulation have attracted great attention in recent years due to the popularity of mobile image and video capturing devices. In our daily life, the most common subject appearing in the captured content are people. Hence, to create images and videos by manipulating appearances or motions of the human character inside these media becomes an important research issue. In this paper, we propose a novel idea that requires the fusion of video and audio intelligence. We develop a system whose inputs are a monocular video captured by a stationary RGBD sensor and a music clip. In the context of a human character performing a music conducting motion, the output is a re-targeted video where the motion of performer is manipulated according to the emotional cues extracted from the music clip. To achieve this goal, our system is decomposed into three major stages. First, one needs to access to the geometric and appearance information pertaining to meaningful and representative targets in the video sequence. Second, a systematic way is needed to reliably classify and identify important emotions from the music. Third, to complete the emotion transfer, one has to manipulate the video targets based on the extracted music emotions.
Spatio-Temporal Learning of Basketball Offensive Strategies
(2015)
Chen, Ching-Hang
;
Liu, Tyng-Luh
;
Wang, Yu-Shuen
;
Chu, Hung-Kuo
;
Tang, Nick C.
Video-based group behavior analysis is drawing attention to its rich applications in sports, military, surveillance and biological observations. The recent advances in tracking techniques, based on either computer vision methodology or hardware sensors, further provide the opportunity of better solving this challenging task. Focusing speci fi cally on the analysis of basketball o ff ensive strategies, we introduce a systematic approach to establishing unsupervised modeling of group behaviors. In view that a possible group behavior (offensive strategy) could be of di ff erent duration and represented by dynamic player trajectories, the crux of our method is to automatically divide training data into meaningful clusters and learn their respective spatio-temporal model, which is established upon Gaussian mixture regression to account for intra-class spatio-temporal variations. The resulting strategy representation turns out to be flexible that can be used to not only establish the discriminant functions but also improve learning the models. We demonstrate the usefulness of our approach by exploring its e ff ectiveness in analyzing a set of given basketball video clips.