Video capture and post-processing technique for approximating 3D projectile trajectory

In this paper we introduce a low-cost procedure and methodology for markerless projectile tracking in three-dimensional (3D) space. Understanding the 3D trajectory of an object in flight can often be essential in examining variables relating to launch and landing conditions. Many systems exist to track the 3D motion of projectiles but are often constrained by space or the type of object the system can recognize (Qualisys, Göteborg, Sweden; Vicon, Oxford, United Kingdom; Opti-Track, Corvallis, Oregon USA; Motion Analysis, Santa Rosa, California USA; Flight Scope, Orlando, Florida USA). These technologies can also be quite expensive, often costing hundreds of thousand dollars. The system presented in this paper utilizes two high-definition video cameras oriented perpendicular to each other to record the flight of an object. A postprocessing technique and subsequent geometrically based algorithm was created to determine 3D position of the object using the two videos. This procedure and methodology was validated using a gold standard motion tracking system resulting in a 4.5 ± 1.8% deviation from the gold standard

A technology platform for automatic high-level tennis game analysis

Sports video research is a popular topic that has been applied to many prominent sports for a large spectrum of applications. In this paper we introduce a technology platform which has been developed for the tennis context, able to extract action sequences and provide support to coaches for players performance analysis during training and official matches. The system consists of an hardware architecture, devised to acquire data in the tennis context and for the specific domain requirements, and a number of processing modules which are able to track both the ball and the players, to extract semantic information from their interactions and automatically annotate video sequences. The aim of this paper is to demonstrate that the proposed combination of hardware and software modules is able to extract 3D ball trajectories robust enough to evaluate ball changes of direction recognizing serves, strokes and bounces. Starting from these information, a finite state machine based decision process can be employed to evaluate the score of each action of the game. The entire platform has been tested in real experiments during both training sessions and matches, and results show that automatic annotation of key events along with 3D positions and scores can be used to support coaches in the extraction of valuable information about players intentions and behaviours

Articulated motion and deformable objects

This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field

Biomechanical analysis and model development applied to table tennis forehand strokes

Table tennis playing involves complex spatial movement of the racket and human body. It takes much effort for the novice players to better mimic expert players. The evaluation of motion patterns during table tennis training, which is usually achieved by coaches, is important for novice trainees to improve faster. However, traditional coaching relies heavily on coaches qualitative observation and subjective evaluation. While past literature shows considerable potential in applying biomechanical analysis and classification for motion pattern assessment to improve novice table tennis players, little published work was found on table tennis biomechanics. To attempt to overcome the problems and fill the gaps, this research aims to quantify the movement of table tennis strokes, to identify the motion pattern differences between experts and novices, and to develop a model for automatic evaluation of the motion quality for an individual. Firstly, a novel method for comprehensive quantification and measurement of the kinematic motion of racket and human body is proposed. In addition, a novel method based on racket centre velocity profile is proposed to segment and normalize the motion data. Secondly, a controlled experiment was conducted to collect motion data of expert and novice players during forehand strokes. Statistical analysis was performed to determine the motion differences between the expert and the novice groups. The experts exhibited significantly different motion patterns with faster racket centre velocity and smaller racket plane angle, different standing posture and joint angular velocity, etc. Lastly, a support vector machine (SVM) classification technique was employed to build a model for motion pattern evaluation. The model development was based on experimental data with different feature selection methods and SVM kernels to achieve the best performance (F1 score) through cross-validated and Nelder-Mead method. Results showed that the SVM classification model exhibited good performance with an average model performance above 90% in distinguishing the stroke motion between expert and novice players. This research helps to better understand the biomechanical mechanisms of table tennis strokes, which will ultimately aid the improvement of novice players. The phase segmentation and normalization methods for table tennis strokes are novel, unambiguous and straightforward to apply. The quantitative comparison identified the comprehensive differences in motion between experts and novice players for racket and human body in continuous phase time, which is a novel contribution. The proposed classification model shows potential in the application of SVM to table tennis biomechanics and can be exploited for automatic coaching

3DLive: A multi-modal sensing platform allowing tele-immersive sports applications

http://www.eusipco2014.org/program/3DLive project is developing a user-driven mixed reality platform, intended for augmented sports. Using latest sens-ing techniques, 3DLive will allow remote users to share a three-dimensional sports experience, interacting with each other in a mixed reality space. This paper presents the multi-modal sensing technologies used in the platform. 3DLive aims at delivering a high sense of tele-immersion among remote users, regardless of whether they are indoors or outdoors, in the context of augmented sports. In this paper, functional and technical details of the first prototype of the jogging scenario are presented, while a clear separation between indoor and outdoor users is given, since different technologies need to be employed for each case.This work was supported by the EU funded project 3DLive, GA 31848