共查询到20条相似文献,搜索用时 15 毫秒
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Multimedia Tools and Applications - The rapid development in the field of computer vision has encouraged researchers to develop vision systems for moving object detection in embedded surveillance... 相似文献
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提出了一种用于图像序列中检测运动目标的优化算法。针对用于室内目标检测的差分法存在着“虚影”噪声,以及用于室外目标检测的背景估计法在对短序列进行检测时,其结果中存在“残像”噪声的问题,揭示并利用两次差分之间的相关性实现了对“虚影”的检测并将其消除,将其引入背景估计法,以消除后者存在的“残像”噪声。实验表明,该方法在目标检测中不仅消除了“虚影”和“残像”噪声,而且检测结果的完整性显著提高。 相似文献
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将运动目标检测的改进方式分为三类。针对固定摄像机的视觉监控系统,提出了一种改进的高斯混合模型算法。通过对方差在高斯混合模型中的作用进行分析,省略方差更新,将方差设为固定值,均值学习率采用固定值。实验结果表明,同传统检测方法相比,改进的算法具有更好的实时性与可靠性。 相似文献
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We propose a robust visual tracking framework based on particle filter to deal with the object appearance changes due to varying illumination, pose variantions, and occlusions. We mainly improve the observation model and re-sampling process in a particle filter. We use on-line updating appearance model, affine transformation, and M-estimation to construct an adaptive observation model. On-line updating appearance model can adapt to the changes of illumination partially. Affine transformation-based similarity measurement is introduced to tackle pose variantions, and M-estimation is used to handle the occluded object in computing observation likelihood. To take advantage of the most recent observation and produce a suboptimal Gaussian proposal distribution, we incorporate Kalman filter into a particle filter to enhance the performance of the resampling process. To estimate the posterior probability density properly with lower computational complexity, we only employ a single Kalman filter to propagate Gaussian distribution. Experimental results have demonstrated the effectiveness and robustness of the proposed algorithm by tracking visual objects in the recorded video sequences. 相似文献
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Jiman Kim Xiaofei Wang Hai Wang Chunsheng Zhu Daijin Kim 《Multimedia Tools and Applications》2013,67(1):311-335
The detection of moving objects under a free-moving camera is a difficult problem because the camera and object motions are mixed together and the objects are often detected into the separated components. To tackle this problem, we propose a fast moving object detection method using optical flow clustering and Delaunay triangulation as follows. First, we extract the corner feature points using Harris corner detector and compute optical flow vectors at the extracted corner feature points. Second, we cluster the optical flow vectors using K-means clustering method and reject the outlier feature points using Random Sample Consensus algorithm. Third, we classify each cluster into the camera and object motion using its scatteredness of optical flow vectors. Fourth, we compensate the camera motion using the multi-resolution block-based motion propagation method and detect the objects using the background subtraction between the previous frame and the motion compensated current frame. Finally, we merge the separately detected objects using Delaunay triangulation. The experimental results using Carnegie Mellon University database show that the proposed moving object detection method outperforms the existing other methods in terms of detection accuracy and processing time. 相似文献
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针对传统混合高斯背景模型在多变场景下因背景模型更新不及时而存在的误检、漏检等不足,提出一种改进算法.该算法首先通过在高斯分布匹配过程中结合帧间差分获取的帧间未变化区域与变化区域判断像素点的区域类别,然后根据不同的像素区域类别执行不同的背景更新策略,使背景的更新及时准确地反映背景的变化.实验结果表明,该改进混合高斯背景模型算法能有效地解决因目标和背景相互转化而出现的拖尾、影子以及运动目标空洞等问题. 相似文献
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Wei Zheng Luhong Liang Hong Chang Cher-Keng Heng Shiguang Shan Xilin Chen 《Image and vision computing》2012
Different classifiers show different sensitivities to translation-variance. The translation-insensitive classifiers are capable of accelerating the detection process by searching over a coarse grid as well as guaranteeing the recall rate. 相似文献
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从序列图像中提取变化区域是运动检测的主要作用,动态背景的干扰严重影响检测结果,使得有效性运动检测成为一项困难工作。受静态图像显著性检测启发,提出了一种新的运动目标检测方法,采用自底向上与自顶向下的视觉计算模型相结合的方式获取图像的空时显著性:先检测出视频序列中的空间显著性,在其基础上加入时间维度,利用改进的三帧差分算法获取具有运动目标的时间显著性,将显著性目标的检测视角由静态图像转换为空时性均显著的运动目标。实验和分析结果表明:新方法在摄像机晃动等动态背景中能较准确检测出空时均显著的运动目标,具有较高的鲁棒性。 相似文献
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基于运动目标检测的视频水印算法研究 总被引:2,自引:0,他引:2
摘要:为了提高视频水印的鲁棒性,提出一种基于运动目标检测技术的算法。通过相邻帧差法提取并标记视频图像序列中的运动目标,并采用图像局部奇异值分解(SVD)算法,实现水印的嵌入和盲提取过程。在仿真实验中,通过计算水印嵌入后图像的峰值信噪比,证明该水印算法具有很好的不可见性和隐蔽性;并使用strimark软件对嵌入水印后图像进行几何攻击,分析水印图像的相关系数,验证本算法具有很好的鲁棒性。 相似文献
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复杂背景下精准的移动目标检测是智能监控系统的重要任务之一,而移动目标检测中,阈值的选择是关键因素之一。传统的固定阈值检测算法很难满足光照等复杂环境的实际需要,利用贝叶斯理论,提出了自适应的动态阈值移动目标检测算法,通过引入前景图像的均值和方差,以及背景图像的均值,获得自适应的动态阈值,用于克服光照等复杂条件的不利影响。实验结果显示,同传统的固定阈值检测算法相比,提出的算法可以有效地克服噪声的影响,并且在复杂环境下具有更好的鲁棒性和稳定性。 相似文献
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Badri Narayan Subudhi 《Pattern recognition letters》2011,32(15):2097-2108
This article addresses a problem of moving object detection by combining two kinds of segmentation schemes: temporal and spatial. It has been found that consideration of a global thresholding approach for temporal segmentation, where the threshold value is obtained by considering the histogram of the difference image corresponding to two frames, does not produce good result for moving object detection. This is due to the fact that the pixels in the lower end of the histogram are not identified as changed pixels (but they actually correspond to the changed regions). Hence there is an effect on object background classification. In this article, we propose a local histogram thresholding scheme to segment the difference image by dividing it into a number of small non-overlapping regions/windows and thresholding each window separately. The window/block size is determined by measuring the entropy content of it. The segmented regions from each window are combined to find the (entire) segmented image. This thresholded difference image is called the change detection mask (CDM) and represent the changed regions corresponding to the moving objects in the given image frame. The difference image is generated by considering the label information of the pixels from the spatially segmented output of two image frames. We have used a Markov Random Field (MRF) model for image modeling and the maximum a posteriori probability (MAP) estimation (for spatial segmentation) is done by a combination of simulated annealing (SA) and iterated conditional mode (ICM) algorithms. It has been observed that the entropy based adaptive window selection scheme yields better results for moving object detection with less effect on object background (mis) classification. The effectiveness of the proposed scheme is successfully tested over three video sequences. 相似文献
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为了增强人脸识别对光照变化的鲁棒性,提出了一种融合多方法的人脸图像光照预处理算法。该算法首先根据改进的自适应平滑算法(IAS)估计出原图像的亮度分量L,再用Retinex算法求得反射分量R,同时对原图像进行局部对比度增强(LCE)处理来增强图像细节;然后采用基于标准差(SD)的加权方法将多种方法有效融合起来;最后采用基于稀疏表示的分类(SRC)算法进行判别归类。在Yale B人脸库上的实验表明,构造的算法识别率高于使用单一预处理算法,而且在训练样本单一、光照环境较差情况下也能取得很好的识别效果,对光照变化有较好的鲁棒性。 相似文献
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Nacer Farajzadeh Aziz Karamiani Mahdi Hashemzadeh 《Multimedia Tools and Applications》2018,77(6):6775-6797
Detecting and tracking moving objects within a scene is an essential step for high-level machine vision applications such as video content analysis. In this paper, we propose a fast and accurate method for tracking an object of interest in a dynamic environment (active camera model). First, we manually select the region of the object of interest and extract three statistical features, namely the mean, the variance and the range of intensity values of the feature points lying inside the selected region. Then, using the motion information of the background’s feature points and k-means clustering algorithm, we calculate camera motion transformation matrix. Based on this matrix, the previous frame is transformed to the current frame’s coordinate system to compensate the impact of camera motion. Afterwards, we detect the regions of moving objects within the scene using our introduced frame difference algorithm. Subsequently, utilizing DBSCAN clustering algorithm, we cluster the feature points of the extracted regions in order to find the distinct moving objects. Finally, we use the same statistical features (the mean, the variance and the range of intensity values) as a template to identify and track the moving object of interest among the detected moving objects. Our approach is simple and straightforward yet robust, accurate and time efficient. Experimental results on various videos show an acceptable performance of our tracker method compared to complex competitors. 相似文献
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为了弥补运动目标检测中传统混合高斯背景模型仅对单个像素建模、运算耗时的不足,通过提取背景时间统计特征和空间区域特征建立模型,针对模型中的高斯分量采用一种改进的分量个数自适应算法,并在此模型基础上,提出一种自适应迭代分块目标检测方法。通过包含区域信息的背景模型检测目标,减少在同一背景区域中目标的误判和漏判。将自适应迭代分块检测算法与背景的区域信息结合,可以在不降低检测精度的前提下大大提高算法执行速度。实验结果表明,相对于传统算法,本文检测法检测结果信噪比更高,目标更加完整,运行速度平均提高了22%。 相似文献
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一种基于立体视觉的运动目标检测算法 总被引:2,自引:1,他引:1
在目标检测中采用立体视觉方法。首先对立体图像对进行匹配求取场景的视差图,再运用基于视差的背景差分法获得含有运动目标的前景区域,最后根据前景区域的视差和位置分布准确定位各运动目标。立体视觉方法有效解决了单目视觉检测方法中的一些难点问题,可以克服光线的变化和阴影干扰对目标检测带来的影响,在多个目标发生部分遮挡时仍能正确区分各运动目标。 相似文献