一种快速有效的视频镜头边界检测方法

本文在分析现有的镜头边界检测方法的基础上,提出了一种层次化镜头边界检测方法.首先我们利用小波变换对信号奇异点的检测能力和对噪声具有较强的鲁棒性,实现了镜头边界的预检测.然后我们通过对候选镜头边界的虚警分析,有效地降低了闪光灯和快速运动对镜头边界检测产生的影响,并实现了镜头突变检测.在渐变类型分类中,我们分别实现了淡入淡出、扫换和溶解等三种重要镜头边界类型的检测.实验结果表明,本文方法能够快速、有效地实现视频镜头边界类型的检测,对运动和光线变化有较强的鲁棒性.     

Modified Kolmogorov-Smirnov metric for shot boundary detection

A new metric extended from the existing Kolmogorov-Smirnov measure is proposed for detecting shot boundaries of video. Exploiting more fine-grained differences between the cumulative histograms of two successive frames, the proposed metric improves both the recall and precision of shot boundary detection. Experiments conducted on a comprehensive video set show that the new metric outperforms other widely-used histogram-based metrics.     

Fast and Efficient Method for Fire Detection Using Image Processing

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision‐based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand‐alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE L*a*b* color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state‐of‐the‐art fire detection method.     

一种基于模糊逻辑的MPEG压缩视频场景转换检测方法

镜头边界的自动检测是实现基于内容的视频检索必不可少的第一步,目前大多数的场景转换检测方法都是基于非压缩视频的,而越来越多的视频数据却以压缩形式存在。本主文提出了一咱新的针对ＭＰＥＧ压缩视频的场景转换检测算法,它利用ＤＣ序列和运动向量计算像素差、直方图差、统计差和具有“真实”运动向量的宏块所占的比例,然后用模糊逻辑对上述参量加以综合隶属度用自适应的方法确定。实验表明这种镜头检测算法具有较高的检出率和     

一种自动选取阈值的视频镜头边界检测算法

镜头边界检测是实现基于内容的视频检索的一个重要步骤.文中介绍了现有的镜头边界检测的基本方法,并针对其不足提出了一种自动选取阈值的、综合考虑颜色和空间特征的镜头边界检测算法.该方法能较好地检测出镜头突变和物体运动以及光线变化等情况,对渐变镜头也能达到检测的目的.实验结果表明算法能够有效地检测出视频镜头边界.     

基于模糊粗糙集的新闻视频镜头边界检测方法

镜头边界检测是实现基于内容的视频检索的一个重要步骤.为了将视频分割成镜头,现有的方法大都是首先提取大量的特征然后构造相异性测度函数.然而,太多的特征就会降低算法的效率.因此,有必要对镜头边界检测的规则进行特征约简.本文将粗糙集中的属性重要性和模糊粗糙集中的分类精度相结合定义了模糊粗糙算子,并构造了相异度检测函数.最后给出了镜头边界检测的一般性规则.由于本文检测方案的自适应性,因此适合于各种类型的新闻视频.用来自中央电视台的3个多小时的新闻视频所做的镜头边界检测实验获得了95.4%的查全率和96.1%的准确率.     

用非监督式聚类进行视频镜头分割

镜头边界检测是基于内容的视频检索首先要解决的问题。研究人员通常将镜头转换分为突变和渐变,并根据各种转换的特点采用不同的检测算法。在研究中发现,视频镜头的抽象程度与对其进行边界划分的精度相关。为此,提出采用非监督式聚类算法,按照给定的相似尺度对视频数据进行自组织和动态分析,完成层次化的镜头分割。该算法侧重于揭示视频的层次结构,能实现不同精度的视频抽象要求。     

Shot boundary detection in the presence of illumination and motion

Detection of gradual transition and the elimination of disturbances caused by illumination change or fast object and camera motion are the major challenges to the current shot boundary detection techniques. These disturbances are often mistaken as shot boundaries. Therefore, it is a challenging task to develop a method that is not only insensitive to various disturbances but also sensitive enough to capture a shot change. To address these challenges, we propose an algorithm for shot boundary detection in the presence of illumination change, fast object motion, and fast camera motion. This is important for accurate and robust detection of shot boundaries and in turn critical for high-level content-based analysis of video. First, the propose algorithm extracts structure features from each video frame by using dual-tree complex wavelet transform. Then, spatial domain structure similarity is computed between adjacent frames. The declaration of shot boundaries are decided based on carefully chosen thresholds. Experimental study is performed on a number of videos that include significant illumination change and fast motion of camera and objects. The performance comparison of the proposed algorithm with other existing techniques validates its effectiveness in terms of better Recall, Precision, and F1 score.     

基于空间-通道注意力的改进SSD目标检测算法

目标检测的任务是精确识别,有效定位出图像中目标物体,且预定义其类别.针对主流目标检测(single shot multibox detector,SSD)算法存在小目标检测准确度不高,检测效率较低等问题,提出一种基于空间-通道注意力机制的SSD目标检测算法(spatial and channel single shot multi-box detector,SC_SSD).通过在SSD深层网络引入空间-通道注意力机制增强高层特征图语义信息,提高算法获取目标物体的细节与位置信息的能力,从而降低漏检率及误检率,并提高小目标物体检测的准确度.此外,利用MobileNetV2中的深度可分离卷积对SSD主干网络(visual geom-etry group network,VGG-16)进行剪枝处理,降低参数量,从而减少训练与检测的时间.在PASCAL VOC 2007数据集上进行实验,本文算法检测的精确度与速度分别为78.9％与59.4 Fps,比SSD算法提升了 3.2％与26.7 Fps,满足实时性需求.算法也优于相比较的其他算法,是一种有效可行的目标检测算法.     

A motion and illumination resilient framework for automatic shot boundary detection

Detecting and locating a desired information in hefty amount of video data through manual procedure is very cumbersome. This necessitates segregation of large video into shots and finding the boundary between the shots. But shot boundary detection problem is unable to achieve satisfactory performance for video sequences consisting of flash light and complex object/camera motion. The proposed method is intended for recognising abrupt boundary between shots in the presence of motion and illumination change in an automatic way. Typically any scene change detection algorithm assimilates time separation in a shot resemblance metric. In this communication, absolute sum gradient orientation feature difference is matched to automatically generated threshold for sensing a cut. Experimental study on TRECVid 2001 data set and other publicly available data set certifies the potentiality of the proposed scheme that identifies scene boundaries efficiently, in a complex environment while preserving a good trade-off between recall and precision measure.     

基于机器学习的压缩域镜头分割技术

为了进行视频结构化和视频内容分析,需要准确有效地提取视频镜头的边界信息.为此提出了一种利用支持向量机(SVM)学习压缩域特征的算法进行镜头边界检测,只需简单译码即可得到MPEG1/2等各类视频流压缩域的特征信息.经TRECVID2005镜头边界检测集的评测,该算法在保证查全率和检测精度的情况下获得了满意的效果.     

基于ORB的镜头边界检测算法

场景中摄像机移动、对象运动、光亮突变等现象的存在导致现有镜头边界检测算法的顽健性不足,为了有效应对这些变化,提出一种基于ORB的镜头边界检测算法,算法通过计算图像帧之间ORB描述子的匹配程度进行镜头边界检测。在检测过程中使用特征点匹配数和匹配率对镜头边界的特点进行描述,并以相似度曲线描述图像帧之间的关系。实验结果表明,该算法可以有效解决上述问题造成的镜头误检、漏检,同时处理速度也获得了明显提升。     

镜头分割在H.264视频压缩编码及视频服务器中的应用

将镜头分割引入H.264编码端,改善其定比率分配帧类型策略,以针对视频内容特性提高编码效果,并提供压缩流的镜头句柄.提出结合时空切片的信息熵度量帧差、以动态阈值探测镜头边界指定为I帧,非边界帧为P或B帧优化编码,并补充闪光灯防误判算法.经验证动态阈值和熵度量法查全查准率比基本方法提高近18%和13%;结合编码psnr提高10%压缩率提高20%.镜头分割准确,有效提高编码性能,对压缩域视频分割和视频服务器也将产生重大意义.     

一种改进的视频镜头切变检测算法

介绍了现有典型切变检测方法,提出了一种改进的镜头切变检测算法,利用像素法和直方图法相结合,有效避免了像素法导致的误检问题和直方图法导致的漏检问题,取得了较好的实验效果.同时引入一种闪光检测方法,避免了因闪光导致的误检问题.     

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false‐positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network.     

基于窗帧差的镜头边界系数模型及其应用

针对视频结构分析提出了一种新的镜头边界系数模型.首先,按照特定的计算方法计算当前帧的窗帧差,使得窗帧差在镜头内与镜头边界呈现不同的特征;然后,采用相应的镜头边界检测算子计算镜头边界相似系数,镜头边界相似系数越大,则该帧越可能是镜头边界;最后,为了提高镜头边界相似系数的检测能力,根据镜头边界相似系数定义了镜头边界系数.镜头边界系数具有良好的特性,可以单独检测镜头边界,也可以与传统的镜头边界检测方法相结合,为镜头边界检测提供了一种鲁棒的框架.实验结果表明,基于镜头边界系数模型的镜头边界检测方法能明显改善镜头边界检测结果.     

几种改进的镜头边界检测方法及其比较

介绍了国内外最新的几种镜头检测算法，分析了几种实用的视频检索系统中普遍采用的镜头边界检测方法，对其中的几种典型算法进行了改进，包括像素比较法、直方图比较法、X^2直方图比较法、区域块直方图比较法，并通过实验对这几种算法检测效果进行了分析比较。实验结果表明，改进后的算法具有较好的镜头检测效果。     

武打片中的动作场景检测方法

本文提出了一种简单有效的方法检测武打片中的动作场景:首先根据动作场景的节奏特点,从影片层次出发,使用镜头长度和MPEG-7运动活力描述符定义了镜头的步调函数来度量节奏,由此定位快节奏区域,找到动作场景的大体位置;之后根据动作场景的内容发展特点,从镜头层次出发,分析快节奏区域及周边的镜头的内容,根据视觉特征确定动作场景的边界点.两个层次(影片和镜头)信息的充分利用使得方法简单易操作,基于压缩视频的处理方法提高了运算速度,实验结果表明了该检测方法的有效性.     

基于Grad-CAM与KL损失的SSD目标检测算法

鉴于Single Shot Multibox Detector（SSD）算法对中小目标检测时会出现漏检甚至错检的情况,提出一种改进的SSD目标检测算法,以提高中小目标检测的准确性.运用Gradient-weighted Class Activation Mapping（Grad-CAM）技术对检测过程中的细节作可视化处理,并以类激活图的形式呈现各检测层细节,分析各检测层的类激活图发现SSD算法中待检测目标的错检以及中小目标的漏检现象与回归损失函数相关.据此,采用Kullback-Leibler（KL）边框回归损失策略,利用Non Maximum Suppression（NMS）算法输出最终预测框.实验结果表明,改进算法相较于已有检测算法具有更高的准确率以及稳定性.