Browsing by Author "Yu, Jen-Yu"

Now showing 1 - 10 of 10

A tempo analysis system for automatic musk accompaniment
(2007)
Chen, Hua-Tsung
;
Hsiao, Ming-Ho
;
Tsai, Wen-Jiin
;
Lee, Suh-Yin
;
Yu, Jen-Yu
This paper proposes a music accompaniment system capable of catching the tempo of music. The original signal is first reduced to a detection function revealing the pulses of music. To induce the tempo and locate beats, a Fourier analysis-based algorithm with high practicality and generality is designed that it is robust to beat strength and not restricted to specific music genres. The regularity and periodicity of music beats are extracted and even the missing beats are recovered. The performance is validated using a comprehensive testing data set, and results in both formal objective experiments and subjective listening evaluations show convincible performance. Moreover, an interactive interface is designed that users are allowed to select an instrument to accompany the music based on the obtained tempo.
Ball tracking and 3D trajectory approximation with applications to tactics analysis from single-camera volleyball sequences
(2012-10-01)
Chen, Hua-Tsung
;
Tsai, Wen-Jiin
;
Lee, Suh-Yin
;
Yu, Jen-Yu
;
資訊工程學系
Providing computer-assisted tactics analysis in sports is a growing trend. This paper presents an automatic system for ball tracking and 3D trajectory approximation from single-camera volleyball sequences as well as demonstrates several applications to tactics analysis. Ball tracking in volleyball video has great complexity due to the high density of players on the court and the complicated overlapping of ball-player. The 2D-to-3D inference is intrinsically challenging due to the loss of 3D information in projection to 2D frames. To overcome these challenges, we propose a two-phase ball tracking algorithm in which we first detect ball candidates for each frame, and then use them to compute the ball trajectories. With the aid of camera calibration, we involve physical characteristics of ball motion to approximate the 3D ball trajectory from the 2D trajectory. The visualization of 3D trajectory and the applications to trajectory-based tactics analysis not only assist the coaches and players in game study but also make game watching a whole new experience. The experiments on international volleyball games show encouraging results. We believe that the proposed framework can be extended and applied to various kinds of sports games.
Baseball Event Semantic Exploring System Using HMM
(2011)
Tsai, Wei-Chin
;
Chen, Hua-Tsung
;
Gu, Hui-Zhen
;
Lee, Suh-Yin
;
Yu, Jen-Yu
Despite a lot of research efforts in baseball video processing in recent years, little work has been done in analyzing the detailed semantic baseball event detection. This paper presents an effective and efficient baseball event classification system for broadcast baseball videos. Utilizing the specifications of the baseball field and the regularity of shot transition, the system recognizes highlight in video clips and identifies what semantic baseball event of the baseball clips is currently proceeding. First, a video is segmented into several highlights starting with a PC (Pitcher and Catcher) shot and ending up with some specific shots. Before every baseball event classifier is designed, several novel schemes including some specific features such as soil percentage and objects extraction such as first base are applied. The extracted mid-level cues are used to develop baseball event classifiers based on an HMM (Hidden Markov model). Due to specific features detection, more hitting baseball events are detected and the simulation results show that the classification of twelve significant baseball events is very promising.
COMPUTER-ASSISTED SELF-TRAINING SYSTEM FOR SPORTS EXERCISE USING KINECTS
(2013)
Chen, Hua-Tsung
;
He, Yu-Zhen
;
Chou, Chien-Li
;
Lee, Suh-Yin
;
Lin, Bao-Shuh P.
Self-training plays an important role in sports exercise. However, if not under the instruction of a coach, improper training postures can cause serious harm to muscles and ligaments of the body. Hence, the development of computer-assisted self-training systems for sports exercise is a recently emerging research topic. In this paper, we propose a Yoga self-training system, entitled YogaST, which aims at instructing the user/practitioner to perform the asana (Yoga posture) correctly and preventing injury caused by improper postures. Involving professional Yoga training knowledge, YogaST analyzes the practitioner's posture from both front and side views using two Kinects with perpendicular viewing directions and assists him/her in rectifying bad postures. The contour, skeleton, and feature axes of the human body are extracted as posture representation. Then, YogaST analyzes the practitioner's posture and presents visualized instruction for posture rectification so that the practitioner can easily understand how to adjust his/her posture.
Dynamic FEC-Distortion optimization for H.264 scalable video streaming
(2007)
Wen, Wei-Chung
;
Hsiao, Hsu-Feng
;
Yu, Jen-Yu
;
資訊工程學系
;
Department of Computer Science
Forward error correction codes have been shown to be a feasible solution either in application layer or in link layer to fulfill the need of Quality of Service for multimedia streaming over the fluctuant channels. In this paper, we propose FEC-distortion optimization algorithms to efficiently utilize the bandwidth for better video quality. The optimization criterions are based on the unequal error protection by taking account of the error drifting problems from both temporal motion compensation and inter-layer prediction of H.264/MTEG-4 AVC scalable video coding. Also, it can adapt to the content-dependent quality contribution of each video frame in a video layer. Lightweight error-concealment is also incorporated with the proposed algorithms for better H.264 SVC streaming. For some applications where either computation might be the bottleneck or the upper bound of non-decodable probability of each video layer is specified, alternative bandwidth allocation algorithm is provided with the trade-off of slight quality degradation.
Efficient data replication for the delivery of high-quality video content over P2P VoD advertising networks
(2011)
Ho, Chien-Peng
;
Yu, Jen-Yu
;
Lee, Suh-Yin
;
資訊工程學系
;
Department of Computer Science
Recent advances in modern television systems have had profound consequences for the scalability, stability, and quality of transmitted digital data signals. This is of particular significance for peer-to-peer (P2P) video-on-demand (VoD) related platforms, faced with an immediate and growing demand for reliable service delivery. In response to demands for high-quality video, the key objectives in the construction of the proposed framework were user satisfaction with perceived video quality and the effective utilization of available resources on P2P VoD networks. This study developed a peer-based promoter to support online advertising in P2P VoD networks based on an estimation of video distortion prior to the replication of data stream chunks. The proposed technology enables the recovery of lost video using replicated stream chunks in real time. Load balance is achieved by adjusting the replication level of each candidate group according to the degree-of-distortion, thereby enabling a significant reduction in server load and increased scalability in the P2P VoD system. This approach also promotes the use of advertising as an efficient tool for commercial promotion. Results indicate that the proposed system efficiently satisfies the given fault tolerances.
EXTRACTION AND REPRESENTATION OF HUMAN BODY FOR PITCHING STYLE RECOGNITION IN BROADCAST BASEBALL VIDEO
(2011)
Chen, Hua-Tsung
;
Chou, Chien-Li
;
Tsai, Wen-Jiin
;
Lee, Suh-Yin
;
Yu, Jen-Yu
In baseball games, different release points of pitchers form several kinds of pitching styles. Different pitching styles possess individual advantages. This paper presents a novel pitching style recognition approach for automatic generation of game information and video annotation. First, an effective object segmentation algorithm is designed to compute the body contour and extract the pitcher's body. Then, star skeleton is used as the representative descriptor of the pitcher posture for pitching style recognition. The proposed approach has been tested on broadcast baseball video and the promising experimental results validate the robustness and practicability.
HUMAN ACTION RECOGNITION BASED ON LAYERED-HMM
(2008)
Wu, Yen-Chieh
;
Chen, Hsuan-Sheng
;
Tsai, Wen-Jiin
;
Lee, Suh-Yin
;
Yu, Jen-Yu
We address the problem of human action understanding of the upper human body from video sequences. Time-sequential images expressing human actions are transformed to sequences of feature vectors containing the configuration of the human body. A human is modeled as a collection of body parts, linked in a kinematic structure. The relation of the joints is used to estimate the human pose. A proposed layered HMM framework decomposes the human action recognition problem into two layers. The first layer models the actions of two arms individually from low-level features. The second layer models the interrelationship of two arm as an action. Experiments with a set of six types of human actions demonstrate the effectiveness of our proposed scheme, and the comparisons with other HMM systems show the robustness.
Pitch-by-pitch extraction from single view baseball video sequences
(2007)
Chen, Hsuan-Sheng
;
Chen, Hua-Tsung
;
Tsai, Wen-Bin
;
Lee, Suh-Yin
;
Yu, Jen-Yu
This paper presents a novel method for reducing a baseball video segment from one batter to next batter into a more compact pitch-by-pitch video by pitching ball trajectory detection. The pitch-by-pitch video shows the complete pitching and batting process and largely reduces the source video data, making pitching analysis of broadcast baseball sequences an easier task. The proposed method has been tested for several long sequences, and promising results are reported.
Resource-aware Replication Strategies in P2P VoD Services with Scalable Video Streams
(2012)
Ho, Chien-Peng
;
Sun, Yu-Chen
;
Yu, Jen-Yu
;
Lee, Suh-Yin
;
資訊工程學系
This article aims to discuss the replication principle that underlies correlations between peer-network conditions and video quality impacts over unstructured peer-to-peer (P2P) video-on-demand (VoD) overlay networks. In particular, the conditions of a network path among peers, such as communication latencies, packet buffer status, and upload bandwidth, can be used to develop failover mechanisms that enable recovery from a failure in part of the P2P VoD system. The failure recovery can be accomplished by using the resource-aware replication strategy, which can deploy high video quality impacts and popular media-data replicas to the movie time-scale of low averaged resource rate and discontinuous area. The simulation results demonstrate that replicas of popular and high video-distortion data are significant and reflect physical demands. In addition, the averaged visual quality of video can be enhanced, and the load imposed on a VoD server can be reduced, through facilitating proper progression of replication strategy.