氏名

テイ ギナ

程 曦娜

職名

助教

所属理工学術院

(情報生産システム研究センター)

論文

Multi-View 3D Ball Tracking with Abrupt Motion Adaptive System Model, Anti-Occlusion Observation and Spatial Density Based Recovery in Sports Analysis

CHENG Xina;IKOMA Norikazu;HONDA Masaaki;IKENAGA Takeshi

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences100(5)p.1215 - 12252017年-2017年

CiNii

詳細

概要:

Significant challenges in ball tracking of sports analysis by computer vision technology are: 1) accuracy of estimated 3D ball trajectory under difficult conditions; 2) external forces added by players lead to irregular motions of the ball; 3) unpredictable situations in the real game, i.e. the ball occluded by players and other objects, complex background and changing lighting condition. With the goal of multi-view 3D ball tracking, this paper proposes an abrupt motion adaptive system model, an anti-occlusion observation model, and a spatial density-based automatic recovery based on particle filter. The system model combines two different system noises that cover the motion of the ball both in general situation and situation subject to abrupt motion caused by external force. Combination ratio of these two noises and number of particles are adaptive to the estimated motion by weight distribution of particles. The anti-occlusion observation model evaluates image feature of each camera and eliminates influence of the camera with less confidence. The spatial density, which is calculated based on 3D ball candidates filtered out by spatial homographic relationship between cameras, is proposed for generating new set of particles to recover the tracking when tracking failure is detected. Experimental results based on HDTV video sequences (2014 Inter High School Men's Volleyball Games, Japan), which were captured by four cameras located at each corner of the court, show that the success rate achieved by the proposals of 3D ball tracking is 99.42%.

Ball State Based Parallel Ball Tracking and Event Detection for Volleyball Game Analysis

CHENG Xina;IKOMA Norikazu;HONDA Masaaki;IKENAGA Takeshi

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences100(11)p.2285 - 22942017年-2017年

CiNii

詳細

概要:

The ball state tracking and detection technology plays a significant role in volleyball game analysis, whose performance is limited due to the challenges include: 1) the inaccurate ball trajectory; 2) multiple numbers of the ball event category; 3) the large intra-class difference of one event. With the goal of broadcasting supporting for volleyball games which requires a real time system, this paper proposes a ball state based parallel ball tracking and event detection method based on a sequential estimation method such as particle filter. This method employs a parallel process of the 3D ball tracking and the event detection so that it is friendly for real time system implementation. The 3D ball tracking process uses the same models with the past work [8]. For event detection process, a ball event change estimation based multiple system model, a past trajectory referred hit point likelihood and a court-line distance feature based event type detection are proposed. First, the multiple system model transits the ball event state, which consists the event starting time and the event type, through three models dealing with different ball motion situations in the volleyball game, such as the motion keeping and changing. The mixture of these models is decided by estimation of the ball event change estimation. Secondly, the past trajectory referred hit point likelihood avoids the processing time delay between the ball tracking and the event detection process by evaluating the probability of the ball being hit at certain time without using future ball trajectories. Third, the feature of the distance between the ball and the specific court line are extracted to detect the ball event type. Experimental results based on multi-view HDTV video sequences (2014 Inter High School Men's Volleyball Games, Japan), which contains 606 events in total, show that the detection rate reaches 88.61% while the success rate of 3D ball tracking keeps more than 99%.

Motion Prejudgment Dependent Mixture System Noise in System Model for Tennis Ball 3D Position Tracking by Particle Filter

Wang, Yuan; Cheng, Xina; Ikoma, Norikazu; Honda, Masaaki; Ikenaga, Takeshi

Proceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016p.124 - 1292016年12月-2016年12月 

DOIScopus

詳細

概要:© 2016 IEEE.In tennis game analysis, the 3D position of ball plays a crucial role in score judgment and player evaluation. When tracking the tennis ball in 3D space, high speed and abrupt motion change of the tennis ball are the main problems which make it difficult to predict the near future course of the ball. Aiming at solving above two problems, we propose a system model based on an elaborated mixture system noise. The mixture system noise consists of general change noise and adaptive abrupt change noise which is dependent on motion prejudgment result of tennis ball. The motion prejudgment method is carried out by the current state of ball and players. The motion of ball is classified into general motion and three abrupt motions, including smash, bounce and hit the net. Experiments based on 13 HDTV video sequences, which were recorded by four cameras located at four corners of the tennis court outside in a cloudy day including two players were used to explore the performance of the proposed method. The tracking success rate is 81.14%, gaining 27.64% improvement compared with conventional work.

Multi-view 3D ball tracking with abrupt motion adaptive system model, anti-occlusion observation and spatial density based recovery in sports analysis

Cheng, Xina; Ikoma, Norikazu; Honda, Masaaki; Ikenaga, Takeshi

IEICE Transactions on Fundamentals of Electronics, Communications and Computer SciencesE100A(5)p.1215 - 12252017年05月-2017年05月 

DOIScopus

詳細

ISSN:09168508

概要:© 2017 The Institute of Electronics, Information and Communication Engineers. Significant challenges in ball tracking of sports analysis by computer vision technology are: 1) accuracy of estimated 3D ball trajectory under difficult conditions; 2) external forces added by players lead to irregular motions of the ball; 3) unpredictable situations in the real game, i.e. the ball occluded by players and other objects, complex background and changing lighting condition. With the goal of multi-view 3D ball tracking, this paper proposes an abrupt motion adaptive system model, an anti-occlusion observation model, and a spatial density-based automatic recovery based on particle filter. The system model combines two different system noises that cover the motion of the ball both in general situation and situation subject to abrupt motion caused by external force. Combination ratio of these two noises and number of particles are adaptive to the estimated motion by weight distribution of particles. The anti-occlusion observation model evaluates image feature of each camera and eliminates influence of the camera with less confidence. The spatial density, which is calculated based on 3D ball candidates filtered out by spatial homographic relationship between cameras, is proposed for generating new set of particles to recover the tracking when tracking failure is detected. Experimental results based on HDTV video sequences (2014 Inter High School Men's Volleyball Games, Japan), which were captured by four cameras located at each corner of the court, show that the success rate achieved by the proposals of 3D ball tracking is 99.42%.

Ball-like observation model and multi-peak distribution estimation based particle filter for 3D Ping-pong ball tracking

Deng, Ziwei; Cheng, Xina; Ikenaga, Takeshi

Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017p.390 - 3932017年07月-2017年07月 

DOIScopus

詳細

概要:© 2017 MVA Organization All Rights Reserved. 3D ball tracking is of great significance to ping-pong game analysis, which can be utilized to applications such as TV content and tactic analysis. To achieve a high success rate in ping-pong ball tracking, the main problems are the lack of unique features and the complexity of background, which make it difficult to distinguish the ball from similar noises. This paper proposes a ball-like observation model and a multi-peak distribution estimation to improve accuracy. For the balllike observation model, we utilize gradient feature from the edge of upper semicircle to construct a histogram, besides, ball-size likelihood is proposed to deal with the situation when noises are different in size with the ball. The multi-peak distribution estimation aims at obtaining a precise ball position in case the partidles' weight distribution has multiple peaks. Experiments are based on ping-pong videos recorded in an official match from 4 perspectives, which in total have 122 hit cases with 2 pairs of players. The tracking success rate finally reaches 99.33%.

Mixture particle filter with block jump biomechanics constraint for volleyball players lower body parts tracking

Xie, Fanglu; Cheng, Xina; Ikenaga, Takeshi

Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017p.302 - 3052017年07月-2017年07月 

DOIScopus

詳細

概要:© 2017 MVA Organization All Rights Reserved. Volleyball player body parts tracking is very important for block or jump height calculation which can be applied to TV contents and tactical analysis. This paper proposes a mixture particle filter with block jump biomechanics constraint based on 3D articulated human model. Using mixture particle filters tracking different body parts can effectively reduce the freedom degree of the human model and make each particle filter track the specific target more accurately. Block jump biomechanics constraint executes adaptive prediction model and likelihood model which can make the particle filter specific for block tracking. The experiments are based on videos of the Final Game of 2014 Japan Inter High School Games of Men's Volleyball in Tokyo. The tracking success rate reached 93.9% for left foot and 93.8% for right foot.

Event state based particle filter for ball event detection in volleyball game analysis

Cheng, Xina; Ikoma, Norikazu; Honda, Masaaki; Ikenaga, Takeshi

20th International Conference on Information Fusion, Fusion 2017 - Proceedings2017年08月-2017年08月 

DOIScopus

詳細

概要:© 2017 International Society of Information Fusion (ISIF). The ball state tracking and detection technology plays a significant role in volleyball game analysis for volleyball team supporting and tactics development. This paper proposes a ball event detection method to achieve high detection rate by solving challenges including: the great variety of event length, the large intra-class difference of one event and the influence caused by ball trajectories. Proposed state vector covers both the event type and the event period length so that the system model can transits various lengths of event period and predicts event types by volleyball game rules. The curve segmental observation model avoids the tracking error influence to evaluate the event period likelihood by referring neighbouring trajectories of the ball. And according to the standard of the ball event, the feature of the distance between the ball and specific court line are extracted to evaluate the ball event type in observation. At last a two-layer estimation method estimates the posterior state which is a joint probability distribution. Experiments of the proposed method implemented on 3D trajectories tracked from multi-view volleyball game videos shows the detection rate reaches 90.43%.

学内研究制度

特定課題研究

Multi-view Videos based Real-time Volleyboll Game Contents Extraction and Analysis

2017年度

研究成果概要:This research targets on the real-time volleyball game content extraction and analysis based on the game videos and...This research targets on the real-time volleyball game content extraction and analysis based on the game videos and computer vision technologies. Based on the past achievements, the real-time 3D ball tracking, and multi-player tracking, in this year we have achieved on three further fields: the physical game data acquisition, real-time implementation, and strategy data acquisition. First, for the physical data acquisition, we achieved ball trajectory based event detection player body parts tracking for block motion, the 3D ping-pong racket tracking and motion detection. Based on these content, 5 international conference paper and one journal paper is published. Second, for the real-time implementation, we have achieved the 3D ping-pong ball tracking, which is published in two international conference papers. Last, for the strategy data acquisition, we published one conference paper on tactical team status detection.