一个机器人足球现场解说系统

介绍了一个在特定领域：机器人足球领域内实现的从行动到语音的自然语言生成－语音合成系统RSC；RSC涉及到了人工智能、自然语言生成和语音合成等先进技术，能像人类足球解说员一样、根据机器人足球比赛的场上情况实时生成合适的解说词，并通过语音合成技术将解说和足球比赛的现场气氛表现出来。     

足球比赛精彩场景的自动分析与提取

提出了基于MPEG压缩域音频流的足球比赛精彩场景自动分析与提取算法．首先直接提取出压缩域音频特征；然后基于提取出来的压缩域特征实现解说音的检测和分割，并且分别识别足球比赛中解说员激动解说和观众激昂欢呼两种类型音频事件；最后通过概率融合生成最终结果，融合结果所对应的比赛片段就是提取出的足球比赛精彩场景．实验结果表明了该算法的可行性．     

足球场地标志线的自动提取

介绍一种从足球比赛录像中自动提取禁区线和球门线的方法．作为足球比赛场景三维重建和视频解说系统的一部分，提取的结果可用于摄像机定标和球场的重建．使用数学形态学、Hough变换、最小二乘法等多种方法，可以从复杂的足球比赛录像中自动提取出所有禁区线和球门线的直线参数．实验结果表明，文中方法对类似背景的直线提取非常有效，可满足摄像机定标和球场重建的需要．     

足球比赛场景的三维重建和动画生成

介绍一个实用的足球比赛场景三维重建系统。该系统通过比赛的录像片段恢复场景的三维模型 ,包括球员的姿态、位置和足球的运动轨迹 ,允许用户在足球场上漫游 ,从任意的视角观看比赛的进行。通过序列图像 ,系统能够生成一段动画 ,重现精彩的比赛片断。系统通过自动和手工相结合的办法实现特征提取和跟踪过程 ,利用线性定标的方法恢复摄像机视点。经过实验证明 ,该系统重建的模型具有较高的精度 ,误差不超过 1%。     

小编物语

ZoRRo聊写作 足球评论员黄健翔在其BLOG中说过：绝对客观的解说应该是这样的——今天给大家带来的是法国队和德国队的比赛……法国队进攻，1号传给4号，4号传给5号，5号传给10号，球 进了！但是，这样的解说，您会听吗？     

FIFA ONLINE2经典防守反击战术讲解

在足球比赛中，实力处于弱势的一方往往在球员能力上无法和对方抗衡，90分钟的比赛过程中也常常被对手压制住狂攻不止。     

基于HMM的足球视频语义分析研究

针对视频高层语义分析问题,文章结合足球比赛的领域知识,按照足球比赛转播,视频编辑的一般规律,根据足球比赛语义事件随机性的特点,选择特定的视频物理特征,应用 HMM (隐马尔科夫模型) 分析视频的语义结构,确定视频和HMM 模型中各元素的对应关系,构建一个基于HMM 的视频语义分析框架,并通过进行足球视频 HMM 参数的训练,得到视频各语义事件的 HMM 模型,达到视频语义自动分析的目的.     

足球视频球员感知跟踪算法

目的 足球比赛视频中的球员跟踪算法为足球赛事分析提供基础的数据支持。但足球比赛中球员跟踪存在极大的挑战：球员进攻、防守和争夺球权时,目标球员可能产生快速移动、严重遮挡和周围出现若干名干扰球员的情况,目前仍没有一种能够完美解决足球比赛中球员跟踪问题的算法。因此如何解决足球场景中的困难,提升球员跟踪的准确度,成为当前研究的热点问题。方法 本文在分析足球比赛视频中球员目标特点的基础上,通过融合干扰项感知的颜色模型和目标感知的深度模型,提出并设计了一种球员感知的跟踪算法。干扰项感知的颜色模型分别提取目标、背景和干扰项的颜色直方图,利用贝叶斯公式得到搜索区域中每个像素点属于目标的似然概率。目标感知的深度模型利用孪生网络计算搜索区域与目标的相似度。针对跟踪漂移问题,使用全局跟踪器和局部跟踪器分别跟踪目标整体和目标上半身,并且在两个跟踪器的跟踪结果出现较大差异的时候分析跟踪器有效性并进行定位修正。结果 在公共的足球数据集上将本文算法与10个其他跟踪算法进行对比实验,同时对于文本算法进行了局部跟踪器的消融实验。实验结果表明,球员感知跟踪算法的平均有效重叠率达到了0.560 3,在存在同队球员和异队球员干扰的情况下,本文算法比排名第2的算法的有效重叠率分别高出3.7%和6.6%,明显优于其他算法,但是由于引入了干扰项感知的颜色模型、目标感知的深度模型以及局部跟踪器等模块增加了算法的时间复杂度,导致本文算法跟踪速度较慢。结论 本文总结了跟踪算法的整体流程并分析了实验结果,认为干扰项感知、目标感知和局部跟踪这3个策略在足球场景中的球员跟踪问题中起到了重要的作用,为未来在足球球员跟踪领域研究的继续深入提供了参考依据。     

分布式网络信息数据防篡改方法研究

为在足球视频中有效的检测与跟踪运动目标,需要对足球比赛视频中目标检测与跟踪算法进行研究。当前采用的算法,在动态场景中,存在运动目标检测与跟踪效果不佳的问题。为此,提出一种基于OpenCV的足球比赛视频中目标检测与跟踪算法。该算法结合平均背景算法将足球比赛视频中目标图像分割为前景区与背景区,计算足球比赛视频每一帧目标图像和背景图像之间差值的绝对差值,同时计算每一个目标图像中像素点的平均值与标准值来建立目标图像背景统计模型,利用TMHI算法对足球比赛视频中目标初始图像进行阈值分割,得到初始分割图像,对分割图像进行中值滤波和闭运算,再使用卡尔曼滤波对分割后的目标图像进行处理,得到镜头中目标的质心位置和目标外界矩形框,然后对足球比赛视频中目标进行跟踪。实验证明,该算法有效的检测与跟踪足球视频中运动目标。     

足球比赛中无球队员的跑位仿真

在足球比赛中,球员大部分时间是无球跑动.因此,如何正确运用无球的跑动参与到进攻与防守当中去,既是现代足球一种技、战术水平高低的体现,也是取得最终比赛胜利的一个重要环节.对足球比赛中无球跑位的重要性和如何进行无球跑位进行阐述,旨在提高足球队员的跑位意识,提升一个队整体的综合实力.对球场进行了分块,应用算法使球员可以确定球所在的分块和球员的无球跑位位置,最后在433阵型下对无球队员的跑位进行了仿真,得到了良好的效果,表明了方法的可行性.     

A Video-Based 3D-Reconstruction of Soccer Games

In this paper we present SoccerMan, a reconstruction system designed to generate animated, virtual 3D views from two synchronous video sequences of a short part of a given soccer game. After the reconstruction process, which needs also some manual interaction, the virtual 3D scene can be examined and 'replayed' from any viewpoint. Players are modeled as so-called animated texture objects, i.e. 2D player shapes are extracted from video and texture-mapped onto rectangles in 3D space. Animated texture objects have shown very appropriate as a 3D representation of soccer players in motion, as the visual nature of the original human motion is preserved. The trajectories of the players and the ball in 3D space are reconstructed accurately. In order to create a 3D reconstruction of a given soccer scene, the following steps have to be executed: 1) Camera parameters of all frames of both sequences are computed (camera calibration). 2) The playground texture is extracted from the video sequences. 3) Trajectories of the ball and the players' heads are computed after manually specifying their image positions in a few key frames. 4) Player textures are extracted automatically from video. 5) The shapes of colliding or occluding players are separated automatically. 6) For visualization, player shapes are texture-mapped onto appropriately placed rectangles in virtual space. SoccerMan is a novel experimental sports analysis system with fairly ambitious objectives. Its design decisions, in particular to start from two synchronous video sequences and to model players by texture objects, have already proven promising.     

The AGILO Robot Soccer Team—Experience-Based Learning and Probabilistic Reasoning in Autonomous Robot Control

This article describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it. According to our model the control system of an autonomous soccer robot consists of a probabilistic game state estimator and a situated action selection module. The game state estimator computes the robot's belief state with respect to the current game situation using a simple off-the-shelf camera system. The estimated game state comprises the positions and dynamic states of the robot itself and its teammates as well as the positions of the ball and the opponent players. Employing sophisticated probabilistic reasoning techniques and exploiting the cooperation between team mates, the robot can estimate complex game states reliably and accurately despite incomplete and inaccurate sensor information. The action selection module selects actions according to specified selection criteria as well as learned experiences. Automatic learning techniques made it possible to develop fast and skillful routines for approaching the ball, assigning roles, and performing coordinated plays. The paper discusses the computational techniques based on experimental data from the 2001 robot soccer world championship.     

Ball tracking and 3D trajectory approximation with applications to tactics analysis from single-camera volleyball sequences

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.     

Automatic initialization for 3D soccer player tracking

As a special application of computer vision, automatic sports video analysis has been studied by some researchers. This sports video analysis via computer vision is a moderately challenging problem: it is more difficult than analyzing a video of a few laboratory members acting as in a simple scenario and is easier than analyzing a video of crowded people at a subway station. So the success of an analysis heavily depends on how much one can exploit the prior information on the sport and setting. The most challenging and important part would be the tracking of players (and ball). With a multi-camera system, 3D tracking is feasible which is much more meaningful than 2D tracking for the analysis. As an initial step of 3D player tracking from multi-view soccer videos, this paper deals with automatic initialization of player positions. Initial 3D positions can be estimated by exploiting some conditions of a soccer match. To make it robust, prior knowledge on the features of players is learnt by support vector machines (SVM). Experimental results show that the proposed system is efficient for general soccer sequences.     

基于VR技术的三维射击游戏开发研究

目前三维游戏中利用了虚拟场景,以便捷的人机交互,为游戏者提供最佳的游戏体验.三维游戏的衍生在很大程度上推动了虚拟现实(VR)技术的更新优化,其涵盖了虚拟现实所有技术.本文基于VR技术设计开发三维射击游戏.首先分析游戏开发需求,其次设计三维VR系统结构与游戏开发流程,再次详细规划三维射击游戏功能模块,最后进行游戏人物、场...     

多摄像机定标恢复足球比赛场景

在足球运动中,分析比赛需要了解足球场与运动员以及足球之间的位置关系.DLT定标是一种常用的算法,通过改进这种摄像机定标算法,从有限的比赛图像中恢复部分摄像机参数,计算出足球运动员相对于足球场的坐标.实验由两幅静态的图像完成了摄像机定标,通过定标参数和图像对运动员的三维坐标进行了恢复,得到了运动员的三维棍图.     

Live 3D Video in Soccer Stadium

This paper proposes a method to realize a 3D video system that can capture video data from multiple cameras, reconstruct 3D models, transmit 3D video streams via the network, and display them on remote PCs. All processes are done in real time. We represent a player with a simplified 3D model consisting of a single plane and a live video texture extracted from multiple cameras. This 3D model is simple enough to be transmitted via a network. A prototype system has been developed and tested at actual soccer stadiums. A 3D video of a typical soccer scene, which includes more than a dozen players, was processed at video rate and transmitted to remote PCs through the internet at 15–24 frames per second.     

基于游戏引擎的三维虚拟漫游系统实现

三维虚拟环境漫游是虚拟现实技术应用的基本方向之一,具有重要的研究意义与应用价值。基于成熟的游戏引擎技术,提出一个具有较强通用性和可扩展性的三维虚拟漫游系统开发框架,并从3D场景快速构建、3D场景实时绘制、高级视觉特效生成和基于物理的场景模拟等四个方面详细介绍了该框架的关键技术实现。通过对系统原型的测试表明,采用该开发框架所构建的三维虚拟漫游系统可在一般的普通主流PC机上达到较好的性能和表现效果。     

Biomimetic control architecture for robotic cooperative tasks

This article proposes some control algorithms to be applied to the MIROSOT robot league architecture. The MIROSOT league soccer game concept is fairly simple: two teams of robots, with 3–5 robots per side, play football autonomously. The ball that the teams play with is an orange golf ball. Above the pitch is a machine vision camera running at 60 frames per second. This camera is linked to a server, which calculates the positions and velocities of each of the robots and the ball, and then determines what each robot should be doing. These instructions are then communicated to the robots over wireless links. In order to develop an efficient control strategy and architecture, the robots have to use strategies from the real human soccer game. Using the software Simi Scout, a suitable analysis of tactics can be extracted from the games. After analyzing the soccer game, a number of attributes are specified and then embedded at different levels. The specified attributes are interconnected, and the analysis of the game is processed for optimization. Using this information, the robot program is adapted and experimental tests/games are played. We comment on the results, and propose an improved control architecture based on practical results.     

个性化定制的虚拟健身系统设计与实现

目的 传统虚拟健身系统存在着沉浸性、可玩性方面的问题,并且缺少对用户的健身指导。提出一种能够改善健身娱乐性、有效性和指导能力的3维虚拟健身系统设计方法。方法 以自然交互界面、个性化深度定制策略和互反馈控制模型理论为基础,加强人与计算机之间的自然语义转换,增加用户对系统的设计控制能力,实时感知用户身体情况并引导用户进行科学健身。另外,依据此框架实现了一套采用健身自行车、Kinect体感摄像头、心率传感器等设备的健身系统。结果 本文提出了一种新的健身系统设计方法,并进行了实现。通过问卷调查,大部分测试者对本文所实现的系统总体评价比较满意并较认可系统新颖性。系统实验结果显示,本文健身系统设计方法对于改善个人健身的娱乐性和有效性有着良好的效果,并具有良好的通用性。结论 通过对测试者的反馈统计表明,系统在交互性和指导能力方面表现出色。