Non-goal scene analysis for soccer video

The broadcast soccer video is usually recorded by one main camera, which is constantly gazing somewhere of playfield where a highlight event is happening. So the camera parameters and their variety have close relationship with semantic information of soccer video, and much interest has been caught in camera calibration for soccer video. The previous calibration methods either deal with goal scene, or have strict calibration conditions and high complexity. So, it does not properly handle the non-goal scene such as midfield or center-forward scene. In this paper, based on a new soccer field model, a field symbol extraction algorithm is proposed to extract the calibration information. Then a two-stage calibration approach is developed which can calibrate camera not only for goal scene but also for non-goal scene. The preliminary experimental results demonstrate its robustness and accuracy.     

足球视频镜头分类方法

根据视频语义分析和视频摘要等应用对于视频数据结构化的需求,提出了一种针对足球视频的镜头分类方法.通过logo模板匹配检测并定位出视频中的慢镜头,对其余的正常比赛部分做镜头边界检测完成视频切分.基于分块的思想,对正常比赛镜头帧计算其各块的场地像素比率值作为特征,利用SVM分类器将正常比赛镜头分为远镜头、中镜头、球员特写或场外镜头3类.至此,整个视频流可以表示为结构化的四类镜头类型标示序列.实验结果表明,该方法在视频切分和镜头类型识别的准确性方面具有良好的效果.     

A vision-based strategy for autonomous aerial refueling tasks

Autonomous aerial refueling is a key enabling technology for both manned and unmanned aircraft where extended flight duration or range are required. The results presented within this paper offer one potential vision-based sensing solution, together with a unique test environment. A hierarchical visual tracking algorithm based on direct methods is proposed and developed for the purposes of tracking a drogue during the capture stage of autonomous aerial refueling, and of estimating its 3D position. Intended to be applied in real time to a video stream from a single monocular camera mounted on the receiver aircraft, the algorithm is shown to be highly robust, and capable of tracking large, rapid drogue motions within the frame of reference. The proposed strategy has been tested using a complex robotic testbed and with actual flight hardware consisting of a full size probe and drogue. Results show that the vision tracking algorithm can detect and track the drogue at real-time frame rates of more than thirty frames per second, obtaining a robust position estimation even with strong motions and multiple occlusions of the drogue.     

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.     

A novel sequence representation for unsupervised analysis of human activities

Formalizing computational models for everyday human activities remains an open challenge. Many previous approaches towards this end assume prior knowledge about the structure of activities, using which explicitly defined models are learned in a completely supervised manner. For a majority of everyday environments however, the structure of the in situ activities is generally not known a priori. In this paper we investigate knowledge representations and manipulation techniques that facilitate learning of human activities in a minimally supervised manner. The key contribution of this work is the idea that global structural information of human activities can be encoded using a subset of their local event subsequences, and that this encoding is sufficient for activity-class discovery and classification.In particular, we investigate modeling activity sequences in terms of their constituent subsequences that we call event n-grams. Exploiting this representation, we propose a computational framework to automatically discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding characterizations of these discovered classes from a holistic as well as a by-parts perspective. Using such characterizations, we present a method to classify a new activity to one of the discovered activity-classes, and to automatically detect whether it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our approach in a variety of everyday environments.     

基于压缩域的关键帧快速提取方法

关键帧提取技术是基于内容检索和视频分析的基础。关键帧的使用减少了视频索引的数据量,同时也为视频摘要和检索提供了一个组织框架。首先介绍了目前的关键帧提取技术,然后提出了一种基于运动特征利用模糊推理算法从MPEG视频流中提取关键帧的方法。由于处理过程是直接从MPEG的压缩视频提取,不需对其解压,所以计算复杂度低,提高了提取速度。实验证明该方法效率高,可以比较好地代表视频内容。     

足球比赛视频中的精彩镜头分析方法

针对足球视频应用的实际需要,文章提出了一种通过视频、音频信息等多种特征的提取和融合来进行足球比赛视频分析的方法。该方法首先在视频中进行镜头探测,然后在单独的镜头中提取颜色特征,进行镜头分类;在所需要的镜头类型中对球场特征、运动对象特征和音频特征进行提取、融合,建立有效的分析方法,从而实现精彩镜头分析。     

基于分布式混合架构体系的高清智能视频分析系统

针对高清视频流数据量大、对处理平台要求高的特点,提出基于DSP+ARM+PC搭建分布式混合架构体系的硬件平台,研制一种经济适用、性能稳定的高清智能视频分析系统,实现对1080 P及以上的高清视频流进行采集、编码、智能分析、报警、压缩、网络化传输等功能,为高清视频图像的智能分析处理提供一体化的解决方案。     

基于GPU的视频流拼接算法研究

为解决视频流的稳定实时拼接,结合图形处理器GPU强大的并行计算能力,提出了一种基于GPU的视频流拼接算法.提取视频流的帧图像,利用尺度不变特征变换(scale invariant feature transform,SIFT)算法在GPU上实现帧图像的特征提取与匹配,实现图像拼接,进而实现视频流的稳定实时拼接.基于GPU的SIFT算法充分利用了GPU的并行处理能力,加快了视频流拼接算法执行的速度,真正意义上实现了几个差异较大但具有公共视野的视频流快速稳定的拼接.     

智能视频监控和智能视频分析技术在检察工作中的应用

介绍了智能视频监控和智能视频分析的基本概念、工作原理及相关技术。分析了当前"监视居住"在司法实践中存在的问题。借鉴智能视频监控技术和智能视频分析技术的成熟应用经验,将该技术应用到"监视居住"中。利用智能视频监控技术和智能视频分析技术实现智能检察,为落实检察机关科技强检战略开辟了一条新思路。     

A low-cost calibration method for automated optical mensuration using a video camera

Automated optical mensuration gauges the acquired image of the inspected unit while assessing its actual size and shape. The mensuration requires the following preparations: (1) alignment of the video camera perpendicularly to the inspection table, and (2) calibration of the scale ratios of image acquisition, notably, the stretching ratio caused by signal conversion and the magnification ratio of optical coupling. This paper presents the unique two-stage calibration method. The first stage applies the parallelogram conservation property, a property very sensitive to misorientation, to test against the potential misalignment. Once detected, we adjust the misalignment towards orthogonal alignment using image patterns of the calibration template. Then, the second stage determines the scale ratios. The proposed calibration method is suitable for on-site applications, and its implementation cost is low. Sensitivity analysis and experimental results are reported.     

A migration strategy for automating a vision-based concentricity inspection station

This paper presents the development of an automation migration strategy in transforming a manual visual inspection work cell into a semi-automated one for a medical device manufacturer in China. The object under study is a washer/magnet subassembly used in an air release valve. These two circular components must be bonded concentrically and then inspected with bare eyes by a human inspector. Such inspection process was prone to error as the inspector struggled to keep up with the production cycle time. The methodology employed in this research consists of four steps. First, we examined the cost of rework through the Pareto analysis. The results indicated that the washer/magnet misalignment accounted for more than 40% of valve defects and thus deserved immediate attention. Next, we conducted two Kappa analyses to evaluate repeatability and reproducibility of the human inspectors assigned to perform the inspection tasks. The results showed that the human inspectors failed to pass these tests and a suitable automation solution must be sought. Afterwards, efforts were made to develop a vision based semi-automated concentricity inspection station to eliminate human inspection errors. Hardware setup, software algorithms, lighting and other supporting devices are presented in this paper as well as potential savings for such an installment. Finally, we conducted an economical analysis to compare the semi-automated solution with a fully automated one to identify the best automation migration strategy. The analysis results showed that the semi-automated solution was a favorable choice due to a shorter payback period and its ease of reinstallation if the factory is to be relocated.     

SAPPHIRE: A toolkit for building efficient stream programs for medical video analysis

This paper describes the design and implementation of SAPPHIRE – a novel middleware and software development kit for stream programing on a heterogeneous system of multi-core multi-CPUs with optional hardware accelerators such as graphics processing unit (GPU). A stream program consists of a set of tasks where the same tasks are repeated over multiple iterations of data (e.g., video frames). Examples of such programs are video analysis applications for computer-aided diagnosis and computer-assisted surgeries. Our design goal is to reduce the implementation efforts and ease collaborative software development of stream programs while supporting efficient execution of the programs on the target hardware. To validate the toolkit, we implemented EM-Automated-RT software with the toolkit and reported our experience. EM-Automated-RT performs real-time video analysis for quality of a colonoscopy procedure and provides visual feedback to assist the endoscopist to achieve optimal inspection of the colon during the procedure. The software has been deployed in a hospital setting to conduct a clinical trial.     

A new approach to vision-based unsupervised learning of unexplored indoor environment for autonomous land vehicle navigation

A vision-based approach to unsupervised learning of the indoor environment for autonomous land vehicle (ALV) navigation is proposed. The ALV may, without human's involvement, self-navigate systematically in an unexplored closed environment, collect the information of the environment features, and then build a top-view map of the environment for later planned navigation or other applications. The learning system consists of three subsystems: a feature location subsystem, a model management subsystem, and an environment exploration subsystem. The feature location subsystem processes input images, and calculates the locations of the local features and the ALV by model matching techniques. To facilitate feature collection, two laser markers are mounted on the vehicle which project laser light on the corridor walls to form easily detectable line and corner features. The model management subsystem attaches the local model into a global one by merging matched corner pairs as well as line segment pairs. The environment exploration subsystem guides the ALV to explore the entire navigation environment by using the information of the learned model and the current ALV location. The guidance scheme is based on the use of a pushdown transducer derived from automata theory. A prototype learning system was implemented on a real vehicle, and simulations and experimental results in real environments show the feasibility of the proposed approach.     

体育视频分析

体育视频因为拥有数量庞大的受众群体和巨大的商业应用前景而备受研究者和工业界的关注.文中从底层特征提取、中级关键字生成、高级语义推理、相关应用研究和原型系统开发等方面,综述了近年来体育视频分析的研究进展以及可能的发展趋势.     

一种基于粗糙集的视频分类方法

视频流的数据量大,又是一种非结构性的数据,因此视频分类一直是视频分析工作中的一个难点。提出了首先进行视频分割,形成了一个视频属性数据库;然后使用粗糙集的属性约简方法对视频属性数据库进行数据挖掘,提取出分类规则集,实现对视频数据库的分类。     

Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites

Modern construction projects require sufficient planning and management of resources to become successful. Core issues are tasks that deal with maintaining the schedule, such as procuring materials, guaranteeing the supply chain, controlling the work status, and monitoring safety and quality. Timely feedback of project status aids project management by providing accurate percentages of task completions and appropriately allocating resources (workforce, equipment, material) to coordinate the next work packages. However, current methods for measuring project status or progress, especially on large infrastructure projects, are mostly based on manual assessments. Recent academic research and commercial development has focused on semi- or fully-automated approaches to collect and process images of evolving worksites. Preliminary results are promising and show capturing, analyzing, and documenting construction progress and linking to information models is possible. This article presents first an overview to vision-based sensing technology available for temporary resource tracking at infrastructure construction sites. Second, it provides the status quo of research applications by highlighting exemplary case. Third, a discussion follows on existing advantages and current limitations of vision based sensing and tracking. Open challenges that need to be addressed in future research efforts conclude this paper.     

A modeling framework of content pollution in Peer-to-Peer video streaming systems

Peer-to-Peer (P2P) live video streaming systems are known to suffer from intermediate attacks due to its inherent vulnerabilities. The content pollution is one of the common attacks that have received little attention in P2P live streaming systems. In this paper, we propose a modeling framework of content pollution in P2P live streaming systems. This model considers both unstructured and structured overlays, and captures the key factors including churns, user interactions, multiple attackers and defensive techniques. The models are verified with simulations and implemented in a real working system, Anysee. We analyze content pollution and its effect in live streaming system. We show that: (1) the impact from content pollution can exponentially increase, similar to the random scanning worms, leading to playback interruption and unnecessary bandwidth consumption; (2) content pollution is influenced by peer cooperation, peer degree and bandwidth in unstructured overlays, and topology breadth in structured ones; (3) the structured overlay is more resilient to content pollution; (4) a hybrid overlay result in better reliability and pollution resistance; (5) hash-based chunk signature scheme is most promising against content pollution.     

城市轨道交通智能视频分析关键技术综述

轨道交通是改善城市公共交通状况的有效途径。随着城市轨道交通的快速建设,人们对城市轨道交通的安全问题越来越重视。智能视频分析技术通过对监控视频流的实时分析,对场景中的各种目标进行检测、分类、跟踪,并分析和判断目标的行为,从而能在异常情况发生时可以及时报警、主动防范,提高处理突发事件的效率。主要研究了智能视频分析技术在轨道交通智能视频监控系统中的应用背景及技术框架,总结了智能视频分析中的关键技术的不同实现方法及其常见的算法。