视频检索中基于GMM聚类的无监督情感场景检测

为了高效地从视频中检索出激动人心的场面,提出了一种基于高斯混合模型的无监督情感场景检测方法.首先,从面部选取42个特征点,并定义10种面部特征;然后,利用高斯混合模型将视频的帧划分为多个聚类;最后,利用每一帧的面部表情分类结果将情感场景划分为单个聚类,并通过场景集成和删除完成检测.在生活记录视频和MMI人脸表情数据库上的实验结果表明,该方法的检测率、分类率分别高达98％,95％,检测5分钟左右的情感场景视频仅需0.138 s,性能优于几种较为先进的检测方法.     

Video scene characteristic detection to improve digital watermarking transparency

A new method of video scene content characteristic detection is proposed. This method can be applied in conjunction with digital watermarking schemes in order to improve the transparency. In addition, new methods of video watermarking are also proposed. Three related original aspects are reported. First, the authors' previous robust image watermarking methods, which consider the block texture are extended into video. In the detection process, watermark extraction with or without the original frame is provided. Secondly, a method is proposed to improve imperceptibility by reducing the flickering effect. Finally, a method is introduced to detect video scene characteristics in order to suit the particular embedding scheme. By doing so, the embedding method can be chosen adaptively in accordance with the video scene. The subjective tests illustrate an imperceptibility improvement and experiments with various attacks show the watermarking robustness.     

视频序列闪烁减弱的一种方法

减弱闪烁是摄像机预处理模块中非常关键的技术之一.提出了一种采用时域滤波来减弱序列闪烁的方法,该方法首先根据闪烁形成的原因得出闪烁的频率,进而设计相应的时域滤波器来减弱闪烁.对于静止或低速变化的场景变换,该方法可以取得很好的效果;对于高速变化的场景,通过设定阈值的方法来判断是否使用滤波器,也能达到较好的效果.     

基于深度学习的远程视频监控异常图像检测

针对复杂场景下远程视频监控图像异常检测困难、传统算法功能单一(仅针对某种特定场景或某种异常图像进行检测)等问题,提出一种基于深度学习的全自动远程视频异常图像检测方法。首先采用Xavier方法对自行设计的卷积神经网络(Convolutional Neural Network,CNN)的参数进行初始化,然后将标准化后的视频差分图送入CNN的输入层,通过特征提取及下采样,最后在CNN的输出层获得远程视频异常图像检测结果。实验结果表明,该方法可以对远程视频监控中突然出现遮挡、模糊和场景切换等多种异常同时进行实时在线检测,准确率可达88.75%。     

基于DVB-S的视频实时监测处理方法

针对视频节日监测采集到的活动与静止的视频场景变化,采用GoP数据量波动率对场景变换进行粗判,并利用I帧和直方图帧差方法进行细判的联合检测算法,能较好检测场景变换,实现视频数据自动监控记录,大大节省了存储空间和工作量.     

Combining scene model and fusion for night video enhancement

This paper presents a video context enhancement method for night surveillance. The basic idea is to extract and fuse the meaningful information of video sequence captured from a fixed camera under different illuminations. A unique characteristic of the algorithm is to separate the image context into two classes and estimate them in different ways. One class contains basic surrounding scene information and scene model, which is obtained via background modeling and object tracking in daytime video sequence. The other class is extracted from nighttime video, including frequently moving region, high illumination region and high gradient region. The scene model and pixel-wise difference method are used to segment the three regions. A shift-invariant discrete wavelet based image fusion technique is used to integral all those context information in the final result. Experiment results demonstrate that the proposed approach can provide much more details and meaningful information for nighttime video.     

Dominant sets based movie scene detection

Multimedia indexing and retrieval has become a challenging topic in organizing huge amount of multimedia data. This problem is not a trivial task for large visual databases; hence, segmentation into low- and high-level temporal video segments might improve the realization of this task. In this paper, we introduce a weighted undirected graph-based movie scene detection approach to detect semantically meaningful temporal video segments. The method is based on the idea of finding the dominant scene of the video according to the selected low-level feature. The proposed method starts from obtaining the most reliable solution first and exploit each solution in the subsequent steps recursively. The dominant movie scene boundary, which can be the highest probability to be the correct one, is determined and this scene boundary information is also exploited in the subsequent steps. We handle two partitioning strategies to determine the boundaries of the remaining scenes. One is a tree-based strategy and the other is an order-based strategy. The proposed dominant sets based movie scene detection method is compared with the graph-based video scene detection methods presented in literature.     

Online video scene clustering by competitive incremental NMF

Efficient clustering and categorizing of video are becoming more and more vital in various applications including video summarization, content-based representation and so on. The large volume of video data is the biggest challenge that this task presents, for most the clustering techniques suffer from high dimensional data in terms of both accuracy and efficiency. In addition to this, most video applications require online processing; therefore, clustering should also be done online for such tasks. This paper presents an online video scene clustering/segmentation method that is based on incremental nonnegative matrix factorization (INMF), which has been shown to be a powerful content representation tool for high dimensional data. The proposed algorithm (Comp-INMF) enables online representation of video content and increases efficiency significantly by integrating a competitive learning scheme into INMF. It brings a systematic solution to the issue of rank selection in nonnegative matrix factorization, which is equivalent to specifying the number of clusters. The clustering performance is evaluated by tests on TRECVID video sequences, and a performance comparison to baseline methods including Adaptive Resonance Theory (ART) is provided in order to demonstrate the efficiency and efficacy of the proposed video clustering scheme. Clustering performance reported in terms of recall, precision and F1 measures shows that the labeling accuracy of the algorithm is notable, especially at edit effect regions that constitute a challenging point in video analysis.     

Scene change detection algorithms for content-based video indexingand retrieval

There is an urgent need to extract key information from video automatically for the purposes of indexing, fast retrieval, and scene analysis. To support this vision, reliable scene change detection algorithms must be developed. Several algorithms have been proposed for both sudden and gradual scene change detection in uncompressed and compressed video. In this paper some common algorithms that have been proposed for scene change detection are reviewed. A novel algorithm for sudden scene change detection for MPEG-2 compressed video is then presented. This uses the number of interpolated macroblocks in B-frames to identify the sudden scene changes. A gradual scene change detection algorithm based on statistical features is also presented     

Automatic foreground extraction from imperfect backgrounds using multi-agent consensus equilibrium

Extracting accurate foreground objects from a scene is an essential step for many video applications. Traditional background subtraction algorithms can generate coarse estimates, but generating high quality masks requires professional softwares with significant human interventions, e.g., providing trimaps or labeling key frames. We propose an automatic foreground extraction method in applications where a static but imperfect background is available. Examples include filming and surveillance where the background can be captured before the objects enter the scene or after they leave the scene. Our proposed method is very robust and produces significantly better estimates than state-of-the-art background subtraction, video segmentation and alpha matting methods. The key innovation of our method is a novel information fusion technique. The fusion framework allows us to integrate the individual strengths of alpha matting, background subtraction and image denoising to produce an overall better estimate. Such integration is particularly important when handling complex scenes with imperfect background. We show how the framework is developed, and how the individual components are built. Extensive experiments and ablation studies are conducted to evaluate the proposed method.     

Motion-based unusual event detection in human crowds

Analyzing human crowds is an important issue in video surveillance and is a challenging task due to their nature of non-rigid shapes. In this paper, optical flows are first estimated and then used for a clue to cluster human crowds into groups in unsupervised manner using our proposed method of adjacency-matrix based clustering (AMC). While the clusters of human crowds are obtained, their behaviors with attributes, orientation, position and crowd size, are characterized by a model of force field. Finally, we can predict the behaviors of human crowds based on the model and then detect if any anomalies of human crowd(s) present in the scene. Experimental results obtained by using extensive dataset show that our system is effective in detecting anomalous events for uncontrolled environment of surveillance videos.     

基于场景复杂度与不变特征的航拍视频实时配准算法

实时、鲁棒的图像配准是航拍视频电子稳像、全景图拼接和地面运动目标自动检测与跟踪的前提和关键技术.本文以航拍视频序列为处理对象,提出了一种新的基于场景复杂度与不变特征的实时配准算法,其主要特点包括：(1)在对航拍视频配准难点进行详细分析的基础上,有针对性的提出基于积分图的快速图像尺度空间构建、依据场景复杂度的检测特征点数量在线精确控制、基于描述子误差分布统计特性级的联分类器构造等新方法,使得算法配准性能不随场景的复杂度发生改变,能够在各种地貌条件下实时、稳定的进行图像配准;(2)将多尺度Harris角点和SIFT描述子相结合,并通过对帧间变换模型参数进行鲁棒估计,保证了算法具有良好的旋转、尺度、亮度不变性和配准精度.实验结果表明,算法可在场景变化、图像大幅度平移、尺度缩放和任意角度旋转等复杂条件下实时、精确的进行图像配准,对分辨率为320×240的航拍序列的平均处理速度达到20.7帧/秒.     

Video traffic prediction based on source information and preventive channel rate decision for RCBR

In this paper, we address the problem of dynamic bandwidth allocation in real-time video transmission. Firstly, a source traffic prediction method is proposed which is based on the rate-distortion relation of source video. This method can detect changes in the source traffic level before encoding by using source information. Secondly, a preventive channel rate decision algorithm, called PCRD, is proposed. The transmission rate bounds are derived from the constraints of the encoder and decoder buffers based on the predicted bit-rate of video frames. From simulation results, the proposed traffic prediction method is shown effective in detecting scene changes and estimating changed traffic levels. Also, the PCRD method is shown to have low renegotiation cost and high channel utilization without violating delay constraints.     

基于场景分割的视频内容语义管理机制

针对视频内容管理在不同层面存在语义鸿沟的问题,提出基于UCL（Uniform Content Locater）的视频语义描述框架,该框架包含了三个层次的语义：内容语义、控制语义以及物理属性信息.而视频场景的分割则通过视频内容基于时空上的相似性实现.对于每个视频场景,结合局部纹理复杂度、背景亮度和场景复杂度,选择最佳参考帧（I帧）与非最佳参考帧（非I帧）以嵌入不同的语义信息：控制语义、物理属性信息嵌入I帧,内容语义嵌入非I帧.利用数字语义水印技术来实现视频内容的语义管理,完成语义信息和载体信号的一体传输和存储.实验中采用JM参考模型进行数字水印方法的验证,结果表明该方法鲁棒性强,且不会造成视频资源质量显著下降.     

一种红外数字场景生成技术

在基于数字微镜器件(Digital Micromirror Device, DMD)的红外景象仿真系统中,需要 提供红外场景数字信号作为用于调制和控制DMD的输入源。为满足此项需求,提出了 一种通用的红外数字场景生成方法。首先介绍了利用计算机生成基于Vega的红外 模拟场景的流程,然后详细论述了开发红外场景驱动控制程序与格式转换程序的 设计过程。该方法不仅可以将Vega红外场景转换为静态BMP格式图像和动态AVI格式视频等 数字信号,而且还可以提高红外仿真场景应用的重用性和可移植 性,因此具有很大的工程实用价值。     

No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.     

Graph-based video fingerprinting using double optimal projection

A double optimal projection method that involves projections for intra-cluster and inter-cluster dimensionality reduction are proposed for video fingerprinting. The video is initially set as a graph with frames as its vertices in a high-dimensional space. A similarity measure that can compute the weights of the edges is then proposed. Subsequently, the video frames are partitioned into different clusters based on the graph model. Double optimal projection is used to explore the optimal mapping points in a low-dimensional space to reduce the video dimensions. The statistics and geometrical fingerprints are generated to determine whether a query video is copied from one of the videos in the database. During matching, the video can be roughly matched by utilizing the statistics fingerprint. Further matching is thereafter performed in the corresponding group using geometrical fingerprints. Experimental results show the good performance of the proposed video fingerprinting method in robustness and discrimination.     

多层平面单应性的丢包事件检测

为及时发现潜在的安全威胁事件,基于多摄像机视图建立多层平面单应性模型,提出一种拥塞环境多视图行人丢包检测方法。提出了行人丢包事件模型,实现了行人跟踪与启发式丢包事件自动检测,并实现丢包事件检测的自动告警。采用真实公众环境的数据集进行测试,结果表明,本文方法有效解决了行人遮挡问题,丢包事件的自动检测鲁棒性较好。