Goal event detection in soccer videos via collaborative multimodal analysis

Detecting semantic events in sports video is crucial for video indexing and retrieval. Most existing works have exclusively relied on video content features, namely, directly available and extractable data from the visual and/or aural channels. Sole reliance on such data however, can be problematic due to the high-level semantic nature of video and the difficulty to properly align detected events with their exact time of occurrences. This paper proposes a framework for soccer goal event detection through collaborative analysis of multimodal features. Unlike previous approaches, the visual and aural contents are not directly scrutinized. Instead, an external textual source (i.e., minute-by-minute reports from sports websites) is used to initially localize the event search space. This step is vital as the event search space can significantly be reduced. This also makes further visual and aural analysis more efficient since excessive and unnecessary non-eventful segments are discarded, culminating in the accurate identification of the actual goal event segment. Experiments conducted on thirteen soccer matches are very promising with high accuracy rates being reported

Research on automatic detection of soccer game events in videos

在计算机视觉领域中，足球事件检测是一个有意义的研究课题。本文提出的算法具有实用价值，可以用于后期的视频片段检索、视频摘要的实时记录和智能手机端视频的实时转码，甚至能有助于更好地理解人们在其他场景下的行为。本文主要研究足球视频中12类事件的自动检测算法，其中重点关注跨时间（基于周期的）事件和具有层级关联事件的检测，此外，还解决了遮挡、足球检测和跟踪等基本问题。所检测的12类事件包括：开球、直接任意球、进攻、渗透、区域防守、层次防守、攻击、压迫、盘带、躲避、跑位和进球。 研究采用的算法包括了视觉特征提取和用于分割帧的场景分类，此有助于减小帧的处理量。在此分类阶段，算法最大限度地利用了球场方位信息...Soccer game events detection is an interesting research topic in computer vision, and the algorithms that are developed during the studying of the games may have valuable helps for different purposes, such as for clip retrieval (posterity logging), for video synopsis(live logging), for real time transcoding for smart phones services, even for a better understanding of people’s behaviors in other c...学位：工学硕士院系专业：信息科学与技术学院_计算机科学与技术学号：2302015115477

The Smart Goal Monitoring System

In the current era of rapid technology development, many researchers compete each other to make an automated and integrated system. Since soccer is a favorite sport of all ages, a goal monitoring. system is very needed, especially goal detection. The goal monitoring system generates fair play and avoids human error on soccer match. It will be very useful to help referee work. The system runs through sensor, image processing, and final decision. Sensor as object reader will activate the camera at many angles. Combining Circle Hough Transform (CHT) with real-rime Color Ball Tracking produces a progressive method to process ball detection. The referees use collaboration tool to get the information. Hence, the referees can be collaborated each other to decide a goal on the match better

Soccer Video Event Detection Via Collaborative Textual, Aural And Visual Analysis

Soccer event detection deals with identifying interesting segments in soccer video via audio/visual content analysis. This task enables automatic high-level index creation, which circumvents large-scale manual annotation and facilitates semantic-based retrieval. This thesis proposes two frameworks for event detection through collaborative analysis of textual, aural and visual features. The frameworks share a common initial component where both utilize an external textual resource, which is the minute-by-minute (MBM) reports from sports broadcasters, to accurately localize sections of video containing the desired events

Team behaviour analysis in sports using the poisson equation

We propose a novel physics-based model for analysing team play- ers’ positions and movements on a sports playing field. The goal is to detect for each frame the region with the highest population of a given team’s players and the region towards which the team is moving as they press for territorial advancement, termed the region of intent. Given the positions of team players from a plan view of the playing field at any given time, we solve a particular Poisson equation to generate a smooth distribution. The proposed distribu- tion provides the likelihood of a point to be occupied by players so that more highly populated regions can be detected by appropriate thresholding. Computing the proposed distribution for each frame provides a sequence of distributions, which we process to detect the region of intent at any time during the game. Our model is evalu- ated on a field hockey dataset, and results show that the proposed approach can provide effective features that could be used to gener- ate team statistics useful for performance evaluation or broadcasting purposes

Robust Playfield Segmentation using MAP Adaptation

A vital task in sports video annotation is to detect and segment areas of the playfield. This is an important first step in player or ball tracking and detecting the location of the play on the playfield. In this paper we present a technique using statistical models, Gaussian mixture models (GMMs) and Maximum a Posteriori (MAP) adaptation. This involves first creating a generic model of the playfield colour and then using unsupervised MAP adaptation to adapt this model to the colour of the playfield in each game. This technique provides a robust and accurate segmentation of the playfield. To demonstrate the robustness of the method we tested it on a number of different sports that have grass playfields, rugby, soccer and field hockey