相似动画视频片段探测方法

为了实现相似视频片段的快速探测,以动画视频片段为研究对象,提出一种建立在视频单元层上的动画视频片段探测方法.在视频特征描述阶段,采用更符合动画图像的Markov平稳特征来描述动画视频帧的视觉特征,并利用视频距离轨迹(VDT)来挖掘视频片段特征,同时采用线性拟合特征的描述方法来描述VDT的特征;在特征匹配阶段,将视频片段匹配问题转换为网络流优化的问题,通过将视频单元的时间一致性嵌入到匹配网络中来寻找最佳对齐方式,大幅度减少了匹配的数据量.实验结果表明,该方法极大地改善了相似视频片段的探测效果,与传统的视频匹配方法相比,其具有更好的鲁棒性以及更高的效率.     

SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection. As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection. In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.     

基于目标变化的监控视频关键帧提取方法

监控视频关键帧提取技术作为监控视频分析的重要研究内容,能够有效地解决视频数据的高效存储和快速访问等问题。本文提出一种基于目标变化的监控视频关键帧提取方法,分析监控视频帧间的目标变化,并采用局部极大值优化方法将原监控视频划分成视频片段。最后,从每个视频片段中选取特征中心对应视频帧作为关键帧,并依据目标的属性删除冗余的关键帧得到最终的视频关键帧集合。实验结果表明,该方法所提取的视频关键帧冗余性较低,所包含的内容很具有代表性。同时,该方法的复杂度较低,适用于监控视频的关键帧提取工作。     

基于拓扑独立成分分析和高斯混合模型的视频语义概念检测

针对目前词袋模型(BoW)视频语义概念检测方法中的量化误差问题,为了更有效地自动提取视频的底层特征,提出一种基于拓扑独立成分分析(TICA)和高斯混合模型(GMM)的视频语义概念检测算法。首先,通过TICA算法进行视频片段的特征提取,该特征提取算法能够学习到视频片段复杂不变性特征;其次利用GMM方法对视频视觉特征进行建模,描述视频特征的分布情况;最后构造视频片段的GMM超向量,采用支持向量机(SVM)进行视频语义概念检测。GMM是BoW概率框架下的拓展,能够减少量化误差,具有良好的鲁棒性。在TRECVID 2012和OV两个视频库上,将所提方法与传统的BoW、SIFT-GMM方法进行了对比实验,结果表明,基于TICA和GMM的视频语义概念检测方法能够提高视频语义概念检测的准确率。     

Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models

We propose a novel unsupervised learning framework to model activities and interactions in crowded and complicated scenes. Hierarchical Bayesian models are used to connect three elements in visual surveillance: low-level visual features, simple "atomic" activities, and interactions. Atomic activities are modeled as distributions over low-level visual features, and multi-agent interactions are modeled as distributions over atomic activities. These models are learnt in an unsupervised way. Given a long video sequence, moving pixels are clustered into different atomic activities and short video clips are clustered into different interactions. In this paper, we propose three hierarchical Bayesian models, Latent Dirichlet Allocation (LDA) mixture model, Hierarchical Dirichlet Process (HDP) mixture model, and Dual Hierarchical Dirichlet Processes (Dual-HDP) model. They advance existing language models, such as LDA [1] and HDP [2]. Our data sets are challenging video sequences from crowded traffic scenes and train station scenes with many kinds of activities co-occurring. Without tracking and human labeling effort, our framework completes many challenging visual surveillance tasks of board interest such as: (1) discovering typical atomic activities and interactions; (2) segmenting long video sequences into different interactions; (3) segmenting motions into different activities; (4) detecting abnormality; and (5) supporting high-level queries on activities and interactions.     

多媒体技术研究:2013——面向智能视频监控的视觉感知与处理

目的 随着视频监控技术的日益成熟和监控设备的普及,视频监控应用日益广泛,监控视频数据量呈现出爆炸性的增长,已经成为大数据时代的重要数据对象。然而由于视频数据本身的非结构化特性,使得监控视频数据的处理和分析相对困难。面对大量摄像头采集的监控视频大数据,如何有效地按照视频的内容和特性去传输、存储、分析和识别这些数据,已经成为一种迫切的需求。方法 本文面向智能视频监控中大规模视觉感知与智能处理问题,围绕监控视频编码、目标检测与跟踪、监控视频增强、视频运动与异常行为识别等4个主要研究方向,系统阐述2013年度的技术发展状况,并对未来的发展趋势进行展望。结果 中国最新制定的国家标准AVS2在对监控视频的编码效率上比最新国际标准H.265/HEVC高出一倍,标志着我国的视频编码技术和标准在视频监控领域已经实现跨越;视频运动目标检测跟踪的研究主要集中在有效特征提取和分类器训练等方面,机器学习等方法的引入,使得基于多实例学习、稀疏表示的运动目标检测跟踪成为研究的热点;监控视频质量增强主要包括去雾、去夜色、去雨雪、去模糊和超分辨率增强等多方面的内容,现有的算法均是对某类图像清晰化效果较好,而对其他类则相对较差,普适性不高;现有的智能动作分析与异常行为识别技术虽然得到了不断发展,算法的性能也在不断提高,但是从实用角度,除了简单的特定或可控场景外,还没有太多成熟的应用系统。结论 随着大数据时代的到来,智能视频监控的需求将日益迫切,面对众多挑战的同时,该研究领域将迎来前所未有的重大机遇,必将产生越来越多可以实用的研究成果。     

基于概率超图的视频事件语义检测

视频事件类别的归属具有模糊性和不确定性,将超图的点边射入矩阵拓展成概率形式的软超图进行关联关系分析和语义分析,将会更有利于提高多事件检索检测的精准率和召回率。提出基于概率超图模型的视频事件语义检测算法（PHVESD）。 该方法首先将颜色、灰度共生矩阵、Tchebichef矩、局部二值模式（LBP）等四种底层视觉特征进行融合; 然后定义视频段的亲密度函数并利用亲密度的信息构建概率超图模型,其中每条超边对应一种事件语义;采用随机游走过程来预测视频段属于每条超边的概率;最后结合阈值采用条件概率模型对视频段进行事件语义分类。将该方法用于交通突发事件多语义检测中并与其他的识别算法相比较,实验结果表明,与基于超图模型的多标签随机游走算法（MLRW）相比,PHVESD的算法使多语义事件检测的准确率提高了10%,召回率提高了8%。     

基于改进的Fisher准则的多示例学习视频人脸识别算法

视频环境下目标的姿态变化使得人脸关键帧难以准确定位,导致基于关键帧标识的视频人脸识别方法的识别率偏低.为解决上述问题,本文提出一种基于Fisher加权准则的多示例学习视频人脸识别算法.该算法将视频人脸识别视为一个多示例问题,将视频中归一化后的人脸帧图像作为视频包中的示例,采用分块TPLBP级联直方图作为示例纹理特征,示例特征的权值通过改进的Fisher准则获得.在训练集合的示例特征空间中,采用多示例学习算法生成分类器,进而实现对测试视频的分类及预测.通过在Honda/UCSD视频库和Youtube Face数据库中的相关实验,该算法达到了较高的识别精度,从而验证了算法的有效性.同时,该方法对均匀光照变化、姿态变化等具有良好的鲁棒性.     

Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system

In this paper, we describe how to detect abnormal human activities taking place in an outdoor surveillance environment. Human tracks are provided in real time by the baseline video surveillance system. Given trajectory information, the event analysis module will attempt to determine whether or not a suspicious activity is currently being observed. However, due to real-time processing constrains, there might be false alarms generated by video image noise or non-human objects. It requires further intensive examination to filter out false event detections which can be processed in an off-line fashion. We propose a hierarchical abnormal event detection system that takes care of real time and semi-real time as multi-tasking. In low level task, a trajectory-based method processes trajectory data and detects abnormal events in real time. In high level task, an intensive video analysis algorithm checks whether the detected abnormal event is triggered by actual humans or not.     

基于视频监控参考量的异常行为检测研究

为了解决传统算法难以检测一般动态场景情形下人体运动目标的问题,文中提出了一种新的人体运动异常行为的检测方法,该方法组合利用视频监控各个的参考量。文中针对视频序列中人的行为进行分析,目的是检测出人的异常行为,具体涉及：人体运动目标的检测、跟踪与提取,异常行为检测等。文中阐述了异常行为检测的相关概念,介绍了视频监控参考量各个参数的计算方法,探讨了异常行为检测与分类技术的关系。结合异常行为检测与分类的相似性,提出了基于视频监控参考量的算法的异常行为检测方法,给出了其计算方法,并确定了检测的过程,分析该方法的特点和优势。     

利用等价关系理论进行视频片段检索的方法

视频片段检索是基于内容的视频检索的主要方式,可是现有的片段检索方法大多只是对预先分割好的片段进行检索。为了从连续的视频节目中自动分割出多个相似的片段,提出了一种新的有效的视频片段检索方法,并首次尝试将等价关系理论应用于视频片段的检索.该方法首先用等价关系理论定义了片段匹配函数,同时采用滑动镜头窗自动分割出多个真正相似的片段;然后把等价类映射为矩阵表达形式,再通过矩阵的特性来度量影响片段相似度的不同因子,实现了相似片段的排序。实验结果表明,该方法能够一次性快速准确地从连续视频库中自动分割出与查询片段相似的多个片段。     

基于机器视觉的嫁接夹抓取点定位与朝向检测

针对现代农业自动化嫁接机的嫁接过程中,嫁接夹的分拣整理仍然需要使用刚性振动盘或人工参与,整体自动化程度有待提高的问题,提出基于机器视觉的嫁接夹轮廓链特征点定位方法与朝向检测;设计了一种自动化嫁接专用的嫁接夹,采用背光源打光方式,对每个阴影轮廓计算Hu矩,根据Hu矩匹配值进行分类以获得正确的嫁接夹个体轮廓;对每个嫁接夹个体轮廓采用轮廓链角分析方法,获得嫁接夹抓取点的像素位置;根据抓取点位置信息计算朝向角度;实验结果表明,嫁接夹抓取点平均定位误差为2.16个像素,嫁接夹朝向角度检测误差为0.40°;该方法可以准确地对嫁接夹进行视觉检测,对嫁接机的嫁接过程中,嫁接夹的自动分拣方面,具有机器视觉方面的价值。     

Efficient human detection in crowded environment

Multimedia Systems - Detecting humans in crowded environment is profitable but challenging in video surveillance. We propose an efficient human detection method by combining both motion and...     

基于帧间差分和边缘差分的遗留物检测算法

遗留物检测是智能视频监控系统的核心功能,遗留物一般较小,所处环境复杂,传统的运动目标检测算法直接用于遗留物检测效果一般.提出了一种基于帧间差分与边缘差分的遗留物检测算法,首先进行帧间差分得到运动目标区域,然后将当前帧图像和前一帧的背景图像进行边缘差分运算得到运动目标的边缘,融合二次差分的结果即可得到运动目标的完整轮廓特征,最终通过判断运动目标在场景中的滞留时间是否达到或超过报警系统设置的阈值来标示遗留物,供智能视频监控系统处理.实验结果证明该算法实时性好且识别率较高.     

Suspicious human activity recognition: a review

Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different abnormal activities recognition; but none of them have addressed different abnormal activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the last decade. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This paper consists of six abnormal activities such as abandoned object detection, theft detection, fall detection, accidents and illegal parking detection on road, violence activity detection, and fire detection. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non-tracking methods, feature extraction, classification; activity analysis and recognition. The objective of this paper is to provide the literature review of six different suspicious activity recognition systems with its general framework to the researchers of this field.     

HMM based soccer video event detection using enhanced mid-level semantic

Highlight detection is a fundamental step in semantics based video retrieval and personalized sports video browsing. In this paper, an effective hidden Markov models (HMMs) based soccer video event detection method based on a hierarchical video analysis framework is proposed. Soccer video shots are classified into four coarse mid-level semantics: global, median, close-up and audience. Global and local motion information is utilized for the refinement of coarse mid-level semantics. Sequential soccer video is segmented into event clips. Both the temporal transitions of the mid-level semantics and the overall features of an event clip are fused using HMMs to determine the type of event. Highlight detection performance of dynamic Bayesian networks (DBN), conditional random fields (CRF) and the proposed HMM based approach are compared. The average F-score of our highlights (including goal, shoot, foul and placed kick) detection approach is 82.92%, which outperforms that of DBN and CRF by 9.85% and 11.12% respectively. The effects of number of hidden states, overall features, and the refinement of mid-level semantics on the event detection performance are also discussed.     

聚众事件的建模与实现

在视频监控领域聚众等异常事件检测有着广泛的应用前景,然而相关研究在国内发展还比较缓慢。在这里给出了基于隐马尔科夫模型的聚众事件的检测方法,其简单过程如下:首先在高斯混合模型检测出目标的基础上,针对聚众事件视频序列的特性,完成了关于帧图像二元组的特征提取;然后,在合理选择初始模型的前提下使用Baum-Welch算法训练聚众事件的隐马尔科夫模型;最后通过实拍的视频序列验证其有效性。