Moving object segmentation in DCT-based compressed video

A block-based automatic segmentation algorithm has been developed for detecting and tracking moving objects in DCT-based compressed video. The proposed algorithm segments moving objects with block resolution using the stochastic behaviour of the image blocks in the DCT domain     

Moving object detection in the H.264/AVC compressed domain for video surveillance applications

In this paper a novel method is presented to detect moving objects in H.264/AVC [T. Wiegand, G. Sullivan, G. Bjontegaard, G. Luthra, Overview of the H.264/AVC video coding standard, IEEE Transactions on Circuits and Systems for Video Technology, 13 (7) (2003) 560–576] compressed video surveillance sequences. Related work, within the H.264/AVC compressed domain, analyses the motion vector field to find moving objects. However, motion vectors are created from a coding perspective and additional complexity is needed to clean the noisy field. Hence, an alternative approach is presented here, based on the size (in bits) of the blocks and transform coefficients used within the video stream. The system is restricted to the syntax level and achieves high execution speeds, up to 20 times faster than the related work. To show the good detection results, a detailed comparison with related work is presented for different challenging video sequences. Finally, the influence of different encoder settings is investigated to show the robustness of our system.     

基于视频序列的运动目标检测

为了能够在数字视频系统中实时的检测出运动目标,提出了一种基于序列图像的运动目标实时检测方法.采用了改进的基于自适应背景提取与自适应阈值分割的背景差分方法提取运动区域,同时使用高斯滤波及形态学滤波消除噪声等,改善了对运动区域的提取效果.实际工程实践证明,该方法可以准确、有效的完成运动目标分割,经统计,检测准确率在95%以上.     

面向检索的压缩域空时视频目标检测与分割

面向语义视频检索,提出一种压缩域的目标分割新算法。它直接基于压缩码流中运动矢量和DCT系数,经过运动检测、矢量分水岭分割、目标融合与修正、后处理与跟踪等步骤提取空时视频目标。整个过程主要基于压缩域进行,无需视频码流的完全解码。对不同测试序列的实验测试结果显示算法能基于压缩域提取较为精确的空时视频目标,并具有较好的鲁棒性。     

利用核密度估计的空基视频运动目标检测

介绍了一种基于核密度估计的空基视频运动目标检测方法.首先,采用特征点对应法进行全局运动估计,根据特征点间的相互位置关系,剔除无效的特征点对,提高全局运动估计精度.并通过背景运动补偿,获取背景相对静止的图像序列;采用非参数核密度估计法进行背景建模,通过背景差分法实现运动目标检测,同时分析了检测噪声产生的主要因素,并采用检...     

Moving object detection and tracking from video captured by moving camera

This paper presents an effective method for the detection and tracking of multiple moving objects from a video sequence captured by a moving camera without additional sensors. Moving object detection is relatively difficult for video captured by a moving camera, since camera motion and object motion are mixed. In the proposed method, the feature points in the frames are found and then classified as belonging to foreground or background features. Next, moving object regions are obtained using an integration scheme based on foreground feature points and foreground regions, which are obtained using an image difference scheme. Then, a compensation scheme based on the motion history of the continuous motion contours obtained from three consecutive frames is applied to increase the regions of moving objects. Moving objects are detected using a refinement scheme and a minimum bounding box. Finally, moving object tracking is achieved using a Kalman filter based on the center of gravity of a moving object region in the minimum bounding box. Experimental results show that the proposed method has good performance.     

Moving video object segmentation using statistical hypothesistesting

Object-based coding is one of the distinct features of the MPEG-4 standard. Hence, video object segmentation is an indispensable process for MPEG-4 coding. A method of localising moving objects for video object segmentation is introduced which utilises a statistical hypothesis test without the need for true statistical parameters to be known a priori     

复杂背景下H.264压缩域运动目标检测算法

针对H.264/AVC压缩码流中的运动目标检测问题,提出了一种基于马尔可夫随机场的最大后验概率(MAP-MRF,maximum a posterior-Markov random field)框架下适合复杂背景的H.264压缩域运动目标检测算法。算法首先生成滤波后的4×4像素块均匀运动矢量(MV,motion vector)场,对MV的相位建立高斯混合模型(GMM,Gaussian mixture model),结合MV幅度、帧间宏块分割模式、MV相位背景模型和运动目标时空约束建立马尔可夫随机场(MRF,Markov random field)模型。通过求解该模型得到每个4×4像素块前景、背景标记检测出运动目标。实验结果表明,算法能从复杂场景的H.264码流中提取出运动目标,与传统算法相比,Precision和Recall指标平均分别提高了20%和3.5%。     

一种针对监控视频场景的压缩域运动对象分割方法

提出了一种压缩域背景建模与运动对象分割框架:首先提取H.264视频压缩码流中的MB-bits与4×4块残差系数,基于MB-bits场进行压缩域Vibe背景建模分割出宏块级运动对象区域,然后结合最大熵模型提取4×4边缘块进行运动对象轮廓细化最终分割出压缩域运动对象。试验对比分析表明,提出的算法能快速、准确地提取压缩域运动对象,系统具有一定鲁棒性。     

压缩域快速视频拷贝检测算法

为了在大规模数据下有效提升拷贝检测速度,提出了一种压缩域视频拷贝检测算法,采用2级匹配框架,利用压缩域上DCT系数顺序度量特征进行相似度匹配,显著减少了查询视频解码操作;并为DCT系数顺序度量特征建立倒排索引,以加快第一级匹配的速度.实验结果表明,与已有的算法相比,所提出算法在保持相当的检测精度条件的同时,大幅提升了检测速度.     

PCA-based approach for video scene change detection on compressed video

An automatic, real-time detection approach to video scene change detection is presented. Owing to the high correlation of two consecutive video frames, it is proposed that only the eigenvector corresponding to the largest eigenvalue is retained in the principal component analysis (PCA) for video data. A one-dimensional PCA feature of video data is then generated from the PCA. It shows superior performance compared to the histogram feature and the pixel feature. The detection algorithm based on this PCA feature is then designed to detect both abrupt and gradual transitions. The proposed approach is tested on the TREC video test repository to validate its performance     

运动视频对象的时空联合检测技术

提出了一种具有全局运动的视频运动对象时空联合检测算法。针对传统时间分割使用主观固定阈值的缺点,采用了对差分图像进行噪声参数自适应学习的算法获取自动阈值,并利用形态学运算获取修正的时间分割模板;考虑传统分水岭空间分割的不足,提出了基于人眼视觉特征的改进分水岭算法,包括基于形态重建滤波的图像降噪、形态梯度变换以及基于韦伯感知原理的视同灰度非线性变换,有效地解决了过分割问题;对时、空间分割结果进行信息融合处理,从而得到完整的运动对象。仿真实验结果表明,本文算法可以快速准确地分割视频运动对象。     

Fast motion detection in compressed domain for video surveillance

A fast algorithm to detect motion in the compressed domain for video surveillance is presented. The algorithm partially decompresses the video bit stream and performs motion detection from its quantised discrete cosine transform coefficients. The very low computational cost makes this algorithm very useful when real-time motion detection has to be performed simultaneously in several video bit streams     

利用改进的背景差法进行运动目标检测

为了更准确地划分运动目标和背景区域,提出了基于背景差法的改进算法。该算法将选择更新法与中值滤波法相结合,对背景建模与阈值选取进行了优化,解决了复杂场景背景建模不理想问题,并能快速有效地进行背景更新。该算法还提出了双阈值思想,根据场景信息自适应地在图像的不同区域采用不同阈值,将当前帧图像与背景图像的差分作为反馈来调整阈值。实验结果表明,该算法能够较快速准确地提取运动目标,并对光照变化、场景变化、运动干扰有较好的鲁棒性。     

一种基于小波分解和分水岭变换的视频对象自动分割算法

提出了一种基于时空分割融合的视频分割算法。时问分割采用变化检测，其关键的阈值选取通过直方图分析得到。空间分割时，先对图像进行二级小波分解，在低分辨率图像上进行分水岭变换，它可以有效克服噪声的影响并有效地减少过分割。实验结果表明，它可比COST211 AM参考分割取得更好的分割结果。     

视频图像中的运动目标检测

运动目标检测,是指从视频图像中将运动变化区域提取出来的检测技术,是图像处理技术的基础。在军事公安、交通管理、视频监控、医学检查等领域应用广泛。为了改进单独采用帧差法或背景减法进行运动目标检测时存在的不足,本文提出一种利用边缘信息的三帧差法与基于混合高斯模型的背景减法相结合的运动目标检测算法。该方法对视频图像中连续的三帧图像两两差分,对3个差分图像取均值,二值化,再经过形态学处理,并对中间帧进行Canny边缘提取,将二者进行"与"运算,即得到运动目标的边缘,用背景减法提取中间帧的前景,二值化,将其和目标的边缘进行"或"运算,经过形态学处理便可得到运动目标。实验结果表明,使用该方法目标检出率提高了9.7%~72.1%,误检率降低了0.090%~2.900%。这种二者相结合的方法相对于单一的检测算法能够有效、可靠地提取出运动目标。     

Text detection,localization, and tracking in compressed video

Video text information plays an important role in semantic-based video analysis, indexing and retrieval. Video texts are closely related to the content of a video. Usually, the fundamental steps of text-based video analysis, browsing and retrieval consist of video text detection, localization, tracking, segmentation and recognition. Video sequences are commonly stored in compressed formats where MPEG coding techniques are often adopted. In this paper, a unified framework for text detection, localization, and tracking in compressed videos using the discrete cosines transform (DCT) coefficients is proposed. A coarse to fine text detection method is used to find text blocks in terms of the block DCT texture intensity information. The DCT texture intensity of an 8×8 block of an intra-frame is approximately represented by seven AC coefficients. The candidate text block regions are further verified and refined. The text block region localization and tracking are carried out by virtue of the horizontal and vertical block texture intensity projection profiles. The appearing and disappearing frames of each text line are determined by the text tracking. The final experimental results show the effectiveness of the proposed methods.     

基于OpenCV的动态背景下的运动目标检测

研究在动态背景下对视频图像序列进行运动目标的检测。主要包括三个步骤,分别是运动估计,运动补偿和目标检测。在运动估计中采用的主要是基于特征点匹配算法。这种算法与传统的块匹配算法最大的好处在于它的数据量少,计算简单迅速而且图像的匹配可靠性更高。最后用计算机视觉库OpenCV进行实现。     

面向多区域视频监控的运动目标检测系统

为了实现视频监控现场多区域运动目标检测,分析了传统运动检测算法的不足,结合帧间差分法和背景差分法,提出背景动态更新的运动检测算法。该算法能自适应背景的变化,减少由背景变化造成的误检测。构建基于FPGA的视频监控系统,在FPGA上用该算法实现了640pixel×480pixel,30帧/s视频信号流的运动目标实时检测。系统提供了分区域运动目标检测的功能。检测区域的大小、位置和个数可通过简单的按键操作进行设定。测试结果表明,系统可以实时地对进入划定区域的运动目标进行检测和闪烁告警,且资源占用较少,适合在小规模的FPGA上进行实现。     

基于嵌入式Linux的运动目标检测系统

选用ARM11(S3C6410)处理器作为硬件平台,嵌入式Linux操作系统作为软件平台,综合运动图像检测的背景差分法和帧间差分法为算法,并与GSM模块相结合,设计并实现了一种应用于家居环境或私人办公室无人值守的运动目标检测系统。提出了系统设计的总体方案,并详细介绍了系统软硬件平台的设计流程,最后对系统进行了测试。