视频序列中运动前景的自动提取方法

在兼顾运动图像分割效果和实时性的基础上,针对视频序列,仅利用其时域信息,提出了一种简单有效的运动前景分割算法。首先对图像序列做帧间差分与隔帧差分,然后将两类差分结果进行累积,对累积结果采取交集聚类的方法求出运动前景轮廓。二值化处理后,扫描填充即可得到图像序列中的运动前景。实验证明：该算法兼顾分割效果和实时性的要求,是一种较好的运动前景分割算法。     

运动目标图像HSI差值模型检测方法研究

本文对自主机器人的运动目标检测和跟踪中的一些关键技术进行了研究,通过传统的帧间差分的改进,引入HSI差值模型、图像序列的连续差分图像运算、自适应分割算法、自适应阴影部分分割算法.实验结果表明该算法有效.     

视频运动对象的自动分割

视频运动对象的分割技术在运动视觉检测和新的MPEG-4视频编码标准中十分重要，提出了一种运动对象分割算法，该算法采用序列图像帧间差的高阶统计量(Higher Order Statistics，HOS)假设检验，确定运动对象的位置，自动分离运动区域与背景；根据三帧序列图像中前后帧差图像灰度边缘重合的部分为中间帧运动对象的边缘来有效地解决运动对象前后帧的遮挡问题；采用形态滤波的方法填充分割出的运动对象二值模板中的空洞，消除残余噪声及平滑边缘，分析和实验证明，该算法需要调整的参数少，抗干扰能力强，可以高效率地进行运动对象的自动分割，此外，该算法具有潜在的并行机制，易于实现实时运动图像处理。     

基于运动信息与多尺度分水岭的视频目标分割

从视频序列中提取视频目标是基于内容编码中的一项关键技术。提出了将高阶统计运动检测和多尺度分水岭相结合的视频目标分割算法。该算法首先利用高阶统计运动检测算法检测出运动区域,通过后处理得到运动目标的初始模板。然后,用小波变换对视频图像进行多分辨率分解。在最低分辨率上应用分水岭算法分割得到具有精确边缘的分割区域,通过将区域融合后的区域逐步投影到高分辨率图像上并结合高分辨率图像上的分水岭算法逐步提取出具有精确边缘的区域。最后,将运动目标的初始模板和多尺度分水岭分割得到的区域结合起来提取出具有精确边缘的视频对象。实验结果表明该算法能有效地分割和提取出视频序列中的视频对象。     

一种视频序列图像中运动目标的分割算法

提出了一种新的用于检测视频序列图像中运动目标的算法。该算法通过对差帧图像进行模板检测及形态学滤波,并结合Canny算子边缘检测获得运动目标的分割掩模,提取出运动目标。实验结果表明,该方法能够有效分割运动物体,具有较强的鲁棒性。     

用于立体视频会议系统的立体对象分割与跟踪算法

基于对象的立体视频编码压缩技术能在立体视频会议系统中得到很好的应用，从立体视频信号中正确分割出立体视频对象是基于对象的立体视频编码压缩的一个前提条件，基于立体视频会议图像序列的时空特性和左右通道间的视差特性，提出了一种立体视频对象分割与跟踪算法，首先利用空域分割和运动检测相结合的方法，提取左通道中的运动物体；然后，提出一种左右通道间基于边缘轮廓的二级视差匹配算法，并根据已分割的左通道运动对象提取右通道的视频运动对象；最后利用对象边界轮廓的跟踪方法对后续图像中的运动对象进行快速跟踪，实验结果说明文中算法能够应用于立体视频会议图像序列的立体对象分割与跟踪。     

基于运动信息与马尔可夫彩色聚类的运动目标分割

针对视频序列图像中的运动目标分割,论文提出了将运动检测和马尔可夫彩色聚类相结合的运动目标分割算法。该算法首先利用基于统计模型的运动检测算法,通过后处理,得到运动目标的初始模板。然后,利用区域生长算法进行彩色图像的初始分割,在初始分割的基础上应用马尔可夫随机场模型进行彩色聚类,得到具有精确边缘的分割区域。最后,将运动目标的初始模板和彩色精确分割结合起来提取出具有精确边缘的运动目标。实验结果表明该算法能有效地分割和提取出视频序列中的运动目标。     

基于图像块差分和Mean Shift算法的运动目标检测

运动目标检测是计算机视觉中的一个重要研究内容,现有算法中的一个重要问题是噪声对分割结果的影响。提出了一种时空域信息相结合的运动目标检测算法：首先利用图像块的重心位置在时间域上差分结果初始化目标轮廓,图像块差分的方法可以消除噪声的影响及减少目标内部的空洞;然后采用Mean Shift算法对初始轮廓进行迭代,使其逐步贴近真实的目标边缘。实验表明该算法能快速准确地分割出序列图像中的运动目标。     

引入区域种子的多运动目标分割算法

针对智能监控系统中对多个运动目标进行图像分割这一问题,提出一种引入区域种子的多运动目标分割算法.算法首先利用背景减算法获得包含多个运动目标的前景图像,再利用四叉树分解方法获得与前景图像对应的稀疏矩阵,通过稀疏矩阵中数值的分布情况,计算出包含运动目标的区域种子点,从这些种子点出发,利用主动轮廓模型进行并行目标轮廓提取,最终完成多运动目标图像分割.实验结果证明本文算法能有效分割出前景图像中多个运动目标,分割结果与人眼视觉的判断相近,并行轮廓提取使算法具有良好的实时性.     

基于图像识别的室外火灾智能报警系统

介绍了一种基于图像识别的室外火灾智能报警系统，包括系统组成和工作流程。该系统运用差值图像的阈值化来识别可疑亮区，并运用膨胀算法对图像中的可疑亮区进行窗口分割。通过序列图像的采集以及可疑亮区的窗口变化。运用人工智能中的规则推理进行火灾的智能识别．     

联合前景和背景对视频对象进行自动分割

提出了一种自动的运动对象分割算法,利用浮点图像的轮廓及其颜色特征将第一帧图像进行区域分割,然后根据帧间运动信息构造出前景和背景图像,最后以前景和背景图像作为参考,对同一场景中所有视频帧进行快速可靠的分割。     

A dynamic conditional random field model for foreground and shadow segmentation

This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences.     

基于图像前景分割的嵌入式智能监控系统

开发了一种嵌入式数字图像监控系统，采用基于背景差的前景分割运算和基于帧间差的动态背景更新算法对监控场景进行前景分割，提取运动目标信息，完成异常判断后，通过网络将异常信息及关键图像序列传送到监控客户端。该文介绍了嵌入式设备的硬件电路、驱动软件和应用程序设计，并给出了网络客户端程序的实现方法。     

时空结合的近景运动目标检测

运动目标检测是智能视频监控中图像序列分析的基础和研究热点,针对时域算法在检测近景大目标缓慢运动时,仅能检测出目标边缘、内部存在大量空洞等完整分割问题,提出了一种结合时空特征的近景运动目标检测算法。该算法在时域运动历史多模态均值背景模型的基础上,运用图像空域信息研究前/背景分割技术,通过能量最小化模型、网络构造及网络流理论,把目标检测转换成最大流/最小割问题。实验表明,该算法能在复杂环境中克服光照缓慢变化、背景扰动和摄像机轻微抖动,有效转换前/背景,准确完整地分割大运动目标。     

自适应混合高斯背景模型的改进

对自适应混合高斯背景模型进行了改进，将背景重构和前景消融时间控制机制整合到传统自适应混合高斯背景模型中，以提高运动分割的质量。背景重构算法从含有运动物体的动态场景视频序列中重构静态背景图像，然后用重构的静态背景图像初始化自适应混合高斯背景模型；而前景消融时间控制机制则使运动物体停止时的前景消融时间独立于背景模型的学习速率，从而可以根据需要调节前景消融的持续时间。实验结果表明了算法的有效性。     

Moving cast shadow detection by exploiting multiple cues

Background subtraction or temporal differencing is commonly applied on an image sequence for foreground/background segmentation. However, cast shadows of moving foreground objects in a scene often result in detection errors for many vision-based applications. To address this problem, the authors propose an algorithm exploiting the information of colour, shading, texture, neighbourhood and temporal consistency to detect shadows efficiently and adaptively. The experimental results show that the proposed method can detect the penumbra as well as the umbra in different kinds of scenarios under various illumination conditions.     

A robust framework for joint background/foreground segmentation of complex video scenes filmed with freely moving camera

This paper explores a robust region-based general framework for discriminating between background and foreground objects within a complex video sequence. The proposed framework works under difficult conditions such as dynamic background and nominally moving camera. The originality of this work lies essentially in our use of the semantic information provided by the regions while simultaneously identifying novel objects (foreground) and non-novel ones (background). The information of background regions is exploited to make moving objects detection more efficient, and vice-versa. In fact, an initial panoramic background is modeled using region-based mosaicing in order to be sufficiently robust to noise from lighting effects and shadowing by foreground objects. After the elimination of the camera movement using motion compensation, the resulting panoramic image should essentially contain the background and the ghost-like traces of the moving objects. Then, while comparing the panoramic image of the background with the individual frames, a simple median-based background subtraction permits a rough identification of foreground objects. Joint background-foreground validation, based on region segmentation, is then used for a further examination of individual foreground pixels intended to eliminate false positives and to localize shadow effects. Thus, we first obtain a foreground mask from a slow-adapting algorithm, and then validate foreground pixels (moving visual objects + shadows) by a simple moving object model built by using both background and foreground regions. The tests realized on various well-known challenging real videos (across a variety of domains) show clearly the robustness of the suggested solution. This solution, which is relatively computationally inexpensive, can be used under difficult conditions such as dynamic background, nominally moving camera and shadows. In addition to the visual evaluation, spatial-based evaluation statistics, given hand-labeled ground truth, has been used as a performance measure of moving visual objects detection.     

Layered Representation of a Video Shot with Mosaicing

: This paper presents a motion segmentation method useful for representing efficiently a video shot as a static mosaic of the background plus sequences of the objects moving in the foreground. This generates an MPEG-4 compliant, layered representation useful for video coding, editing and indexing. First, a mosaic of the static background is computed by estimating the dominant motion of the scene. This is achieved by tracking features over the video sequence and using a robust technique that discards features attached to the moving objects. The moving objects get removed in the final mosaic by computing the median of the grey levels. Then, segmentation is obtained by taking the pixelwise difference between each frame of the original sequence and the mosaic of the background. To discriminate between the moving object and noise, temporal coherence is exploited by tracking the object in the binarised difference image sequence. The automatic computation of the mosaic and the segmentation procedure are illustrated with real sequences experiments. Examples of coding and content-based manipulation are also shown. Received: 31 August 2000, Received in revised form: 18 April 2001, Accepted: 20 July 2001     

Change detection by probabilistic segmentation from monocular view

We present a method for foreground/background video segmentation (change detection) in real-time that can be used, in applications such as background subtraction or analysis of surveillance cameras. Our approach implements a probabilistic segmentation based on the Quadratic Markov Measure Field models. This framework regularizes the likelihood of each pixel belonging to each one of the classes (background or foreground). We propose a new likelihood that takes into account two cases: the first one is when the background is static and the foreground might be static or moving (Static Background Subtraction), the second one is when the background is unstable and the foreground is moving (Unstable Background Subtraction). Moreover, our likelihood is robust to illumination changes, cast shadows and camouflage situations. We implement a parallel version of our algorithm in CUDA using a NVIDIA Graphics Processing Unit in order to fulfill real-time execution requirements.