A review of vision-based systems for soccer video analysis

This paper presents a survey of soccer video analysis systems for different applications: video summarization, provision of augmented information, high-level analysis. Computer vision techniques have been adapted to be applicable in the challenging soccer context. Different semantic levels of interpretation are required according to the complexity of the corresponding applications. For each application area we analyze the computer vision methodologies, their strengths and weaknesses and we investigate whether these approaches can be applied to extensive and real time soccer video analysis.     

Using Webcast Text for Semantic Event Detection in Broadcast Sports Video

Sports video semantic event detection is essential for sports video summarization and retrieval. Extensive research efforts have been devoted to this area in recent years. However, the existing sports video event detection approaches heavily rely on either video content itself, which face the difficulty of high-level semantic information extraction from video content using computer vision and image processing techniques, or manually generated video ontology, which is domain specific and difficult to be automatically aligned with the video content. In this paper, we present a novel approach for sports video semantic event detection based on analysis and alignment of webcast text and broadcast video. Webcast text is a text broadcast channel for sports game which is co-produced with the broadcast video and is easily obtained from the web. We first analyze webcast text to cluster and detect text events in an unsupervised way using probabilistic latent semantic analysis (pLSA). Based on the detected text event and video structure analysis, we employ a conditional random field model (CRFM) to align text event and video event by detecting event moment and event boundary in the video. Incorporation of webcast text into sports video analysis significantly facilitates sports video semantic event detection. We conducted experiments on 33 hours of soccer and basketball games for webcast analysis, broadcast video analysis and text/video semantic alignment. The results are encouraging and compared with the manually labeled ground truth.      

Multimodal feature extraction and fusion for semantic mining of soccer video: a survey

This paper presents a classified review of soccer video analysis works. The existing approaches in the aspects of highlight event detection, video summarization and retrieval based on video stream, ball and player tracking for provision of match statistics, technical and tactical analysis and application of different sources in soccer video analysis have been surveyed. In addition, some major existing commercial softwares developed for video analysis are introduced and compared. With regard to the existing challenge for automatic and realtime provision of video analysis, different computer vision approaches are discussed and compared. Audio, video and text feature extraction methods have been investigated and the future trends for improvement of the reviewed systems have been introduced in terms of response time optimization, increase of precision and eliminating the need of human intervention for video analysis.     

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.     

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.     

基于自适应背景模型运动目标检测

随着城市化速度的加快,机动车日益普及,人们在享受机动车所带来的巨大便利的同时,也面临着交通拥挤的困扰。随着计算机硬件技术和计算机视觉技术的发展,基于计算机视觉的交通监控系统成为可能。从一个交通视频序列中识别出运动物体是许多交通监控系统应用系统的重要任务,针对该问题,提出了一种建立在对视频序列中的整个背景情景的统计描述基础上的运动目标的检测的有效方法,该方法能够适应变化的背景,具有较强的鲁棒性和较好的实时性。     

Features extraction for soccer video semantic analysis: current achievements and remaining issues

This paper presents a state of the art review of features extraction for soccer video summarization research. The all existing approaches with regard to event detection, video summarization based on video stream and application of text sources in event detection have been surveyed. As regard the current challenges for automatic and real time provision of summary videos, different computer vision approaches are discussed and compared. Audio, video feature extraction methods and their combination with textual methods have been investigated. Available commercial products are presented to better clarify the boundaries in this domain and future directions for improvement of existing systems have been suggested.     

Accurate ball detection in soccer images using probabilistic analysis of salient regions

Automatic sport video analysis has became one of the most attractive research fields in the areas of computer vision and multimedia technologies. In particular, there has been a boom in soccer video analysis research. This paper presents a new multi-step algorithm to automatically detect the soccer ball in image sequences acquired from static cameras. In each image, candidate ball regions are selected by analyzing edge circularity and then ball patterns are extracted representing locally affine invariant regions around distinctive points which have been highlighted automatically. The effectiveness of the proposed methodologies is demonstrated through a huge number of experiments using real balls under challenging conditions, as well as a favorable comparison with some of the leading approaches from the literature.     

一种基于基本语义单元合成Petri网的足球视频查询描述模型

足球是最具世界性的体育运动之一，球迷遍布五大洲，因此在体育视频节目中足球备受广大观众青睐．在分析了足球视频特点的基础上，提出了一种基于基本语义单元合成Petri网的足球视频查询描述模型．该模型首先定义了一种类似文本字词集合的足球视频基本语义单元集合，在此基础上采用基本语义单元合成Petri网模型建立了一种足球查询语义的描述模型，并分别构建了进球、进攻、角球、犯规、换人等足球语义．初步的实验结果验证了该模型的有效性，并能推广至球类视频和其他体育视频．     

基于HMM的足球视频语义结构分析

提出了一种足球视频的语义结构,即足球视频由多个语义事件构成,每个语义事件由数个语义镜头组成。为了分析这种语义结构,建立了“精彩事件”和“一般事件”两种语义事件的多个隐马尔科夫模型(HMMs),并提出了场地比率、人脸比率、边缘、运动强度四种特征作为HMMs的观测值输入。利用HMM的三种算法训练HMMs,分析出精彩事件,并为每个镜头标注语义。     

视频中运动目标的实时检测和跟踪

基于视频的自动目标检测和跟踪是计算机视觉中一个重要的研究领域，特别是基于视频的智能车辆监控系统中的运动车辆的检测和跟踪。提出了一种自适应的背景相减法来分割运动物体，为了准确地定位运动车辆的区域，采用差分图像投影和边缘投影相结合的方法来定位车体，同时利用双向加权联合图匹配方法对运动车辆区域进行跟踪，即将对运动车辆区域跟踪问题转化为搜索具有最大权的联合图的问题。该算法不仅能实时地定位和跟踪直道上运动的车辆，同时也能实时地定位和跟踪弯道上运动的车辆，从实验结果看，提出的背景更新算法简单，并且运动车辆区域的定位具有很好的鲁棒性，从统计的检测率和运行时间来看，该算法具有很好的检测效果，同时也能满足基于视频的智能交通监控系统的需要。     

基于视觉的互动游戏手势界面工具箱

分析了视觉手势的交互特征,提出了非接触型设备交互模型,基于数据流图方法建立了支持连续信息输入的数据流模型来描述视觉手势信息的处理流程,基于"paint-view-correct"隐喻构建了手势界面开发工具IEToolkit.系统的主要特点包括:使用插件设计思想提供了可扩展的接口,方便开发人员对系统进行扩展:实现了对平台...     

背景估计与运动目标检测跟踪

基于视频的自动目标检测和跟踪是计算机视觉中一个重要的研究领域,特别是基于视频的智能车辆监控系统中的运动车辆的检测和跟踪。提出一种自适应的背景估计方法来实时获得当前背景图像,从而分割出运动物体。为了准确地定位运动车辆的区域,采用差分图像投影和边缘投影相结合的方法来定位车体,同时利用双向加权联合图匹配方法对运动车辆区域进行跟踪,即将对运动车辆区域跟踪问题转化为搜索具有最大权的联合图的问题。该算法不仅能实时地定位和跟踪直道上运动的车辆,同时也能实时地定位和跟踪弯道上运动的车辆,从实验结果看,提出的背景更新算法简单,并且运动车辆区域的定位具有很好的鲁棒性,从统计的检测率和运行时间来看,该算法具有很好的检测效果,同时也能满足基于视频的智能交通监控系统的需要。     

A Fuzzy Mechanism for Action Selection of Soccer Robots

A vision-based soccer robot system is proposed, in which vision will identify the position and heading angle of each robot, and the position of the ball. With these imaging data, values for the defense factor, the competition factor, and the angle factor, are obtained. Using the three factors as the input variables of the proposed action selection mechanism, which is expressed in terms of fuzzy rules, the action that each robot should take is then chosen from the five basic actions available for the robots. In this manner, each robot may intercept, shoot, block, sweep the ball, or just stand by. After determining the action of each robot, control commands generated by the host computer are sent to the robot directly through a wireless RS-232. To show the feasibility of the proposed method, experimental results of a robot soccer game will be used for illustration.     

Extracting semantics from audio-visual content: the final frontier in multimedia retrieval

Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular, we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review the state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and the various mechanisms for modeling concepts and context.     

一种足球视频精彩事件检测方法

限于当前的技术水平,视频检索技术难以在底层特征与高层语义之间建立通用的视频分析模型。文中结合足球视频的领域知识,着重分析了一类特殊的语义事件——精彩事件,基于统计的方法提出了动态贝叶斯网络事件检测模型,以及相应的学习和推理算法。实验结果表明,该方法可有效地提取足球视频中的精彩语义事件,具有较高的查全率和查准率,较强的鲁棒性,是一种很有前景的视频语义事件检测方法;同时证明了,通过结合某一领域知识,底层特征与高层语义之间是可以建立起某种联系的。     

一种通用的基于基本语义单元的体育视频内容分析框架

体育视频内容分析的研究现状集中在语义标注，对体育视频的句法分段和框架的研究较少．本文在分析了体育视频基本特征的基础上，提出了体育视频中基本语义单元(Basic Semantic Unit，简称BSU单元)的概念；继而提出了一种基于BSU的体育视频内容分析的通用框架；并且以足球视频为例，实例化了这种通用的体育视频内容分析框架．初步的实验结果表明，这种基于BSU的体育视频内容分析框架是有效和可行的．     

Tracking soccer players using spatio-temporal context learning under multiple views

With the popularity of soccer games and rapid development of computer technology, automatic soccer analysis systems have been studied a lot these years. Tracking soccer players, as the fundamental step in an analysis system, is of great research value and draws attention from researchers all over the world. In this paper, we propose an effective method which makes an improvement on spatiotemporal context learning and increases the accuracy by combining information from multiple views. At the same time, a two-dimensional plane graph is displayed to show the players’ movements correspondingly. Experiments are conducted on several video fragments and the results have shown that the proposed method reaches a relatively high accuracy even when there are heavy occlusions and pose variations.     

多模态体育视频语义分析

以足球运动为例提出了一种体育视频语义结构,并提出相应的语义分析框架。视频被分解为纯视频流和音频流两种模态,每种模态均可依次提取和综合出低层内容和中层内容。视频流可根据低层（物理）内容分割为物理镜头,然后根据特定的中间层内容可以确定为语法镜头。音频也可以在物理特征的基础上形成有意义的中间层内容,如解说员兴奋时的声音。最后,根据视频流和音频流的中间层内容,按照足球比赛转播的规律,分析出比赛中的精彩事件,并选取相关的镜头作为反映此事件的序列组合。