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基于差分交集的视频对象分割与跟踪算法 总被引:3,自引:0,他引:3
视频对象分割算法的性能好坏将直接影响MPEG 4编码产品的质量。连续两次差分后自适应处理,对差分图像取交集获得运动对象的边界,形态学处理后最终获取运动目标。基于改进的Hausdorff距离度量法对后续帧中视频对象进行跟踪。实验结果证明,该方法能够从背景不变的图像序列中较好的提取出运动对象,具有较强的鲁棒性。 相似文献
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提出了一种空域和时域相结合的视频显著性检测算法.对单帧图像,受视觉皮层层次化感知特性和Gestalt视觉心理学的启发,提出了一种层次化的静态显著图检测方法.在底层,通过符合生物视觉特性的特征图像(双对立颜色特征及亮度特征图像)的非线性简化模型来合成特征图像,形成多个候选显著区域;在中层,根据矩阵的最小Frobenius-范数(F-范数)性质选取竞争力最强的候选显著区域作为局部显著区域;在高层,利用Gestalt视觉心理学的核心理论,对在中层得到的局部显著区域进行整合,得到具有整体感知的空域显著图.对序列帧图像,基于运动目标在位置、运动幅度和运动方向一致性的假设,对Lucas-Kanade算法检测出的光流点进行二分类,排除噪声点的干扰,并利用光流点的运动幅度来衡量运动目标运动显著性.最后,基于人类视觉对动态信息与静态信息敏感度的差异提出了一种空域和时域显著图融合的通用模型.实验结果表明,该方法能够抑制视频背景中的噪声并且解决了运动目标稀疏等问题,能够较好地从复杂场景中检测出视频中的显著区域. 相似文献
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针对动态环境下运动目标检测中噪声多、目标检测不完整等情况,提出了一种基于金字塔多分辨率模型的运动目标检测方法,在低分辨率下获取目标的区域,在高分辨率下获取目标的细节。对于复杂的环境,还提出了运用高低双阈值替代传统的单阈值进行图像差分运算的方案,阈值可以根据图像自动分析得到。该方法首先将当前帧和背景帧进行尺度变换,得到不同分辨率下的图像组,然后在不同尺度下得到高低阈值差分图像,最后从高层向低层进行有效融合,得到噪声少的完整目标图像。实验表明,该方法提取运动目标的精度比较高,单目标达到0.802,多目标达到0.615,尤其是在复杂的动态环境下,优势比较明显。 相似文献
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《光子学报》2015,(6)
针对场景中存在前景目标运动的抖动视频,提出一种抗前景干扰的自适应电子稳像算法.算法以视觉对运动的感知为指导,采用基于块的三帧间差分,利用时空一致性快速剔除运动前景区域;改进传统Harris算子,用网格筛选和显著度排序,对背景区域进行全局显著特征点的提取和配准,保证全局配准准确度;用统计分布的距离准则去除误匹配点,无需特征点迭代运算,提高了全局运动估计的速度和准确度.在Sage-Husa自适应运动滤波方法的基础上,改进了修正过程噪音和观测噪音的统计特性,模拟摄像机低频匀速运动的视觉平滑效果,有效解决摄像机抖动中存在的扫描运动.在Intel酷睿2四核2.33GHz的微机上用VC++进行实验,结果表明,该算法对320×240像素的视频序列能够达到22fps的处理能力,可以实时稳定含较大或多运动前景目标的复杂抖动视频,输出视觉完整流畅的真实扫描场景. 相似文献
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运动目标检测是智能视频监控系统中的重要步骤和前提。提出了一种基于随机背景建模的非参数化建模算法,对场景中运动目标进行快速提取跟踪。在初始化阶段,从当前像素的邻域中随机抽取样本值作为背景模型;在模型更新阶段,引入了随机更新策略和背景传播机制,能够较好地抑制环境噪声;在后处理阶段,给出了一种基于积分图的前景滤波优化方法,进一步滤除噪声和填充前景空洞。实验结果表明,在复杂场景条件下,算法的目标检测性能明显优于其他几种同类算法,能够较好地抑制噪声干扰,具有较高的检测正确率。对于360288像素的测试视频,算法的计算速度高达120 f/s,完全可以满足实时应用。 相似文献
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This paper presents vehicle detection and tracking algorithms based on real-time background (RTB) and phase-correlation (PC) in the video sequence of urban highway with fixed camera. Firstly moving objects are obtained by subtracting RTB from serial images. After classification, PC is used to determine corresponding regions of moving objects between consecutive frames. The problems of vehicles' merging and splitting, sudden stop, and restart are also considered. Experiments show that the method is practical and can realize real-time detection and tracking of vehicles on highway. 相似文献
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This paper proposes a robust method to detect and extract silhouettes of foreground objects from a video sequence of a static
camera based on the improved background subtraction technique. The proposed method analyses statistically the pixel history
as time series observations. The proposed method presents a robust technique to detect motions based on kernel density estimation.
Two consecutive stages of the k-means clustering algorithm are utilized to identify the most reliable background regions and decrease the detection of false
positives. Pixel and object based updating mechanism for the background model is presented to cope with challenges like gradual
and sudden illumination changes, ghost appearance, non-stationary background objects, and moving objects that remain stable
for more than the half of the training period. Experimental results show the efficiency and the robustness of the proposed
method to detect and extract the silhouettes of moving objects in outdoor and indoor environments compared with conventional
methods. 相似文献
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This paper proposes a self-adaptive optical flow method to detect moving objects in the video sequences. The method first estimates the original optical flow field with the optical flow algorithm, and then enhances the objects by a local mean algorithm, and finally filters out the noise with a self-adaptive threshold algorithm. The proposed method has a wide adaptivity to the size and the number of objects, and it also can effectively process the scenarios of complex background and that of the slight occlusion. Furthermore, it avoids the complicated and time-consuming preprocessing procedure. The results of the present method show that the moving objects can be detected effectively. 相似文献
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Extracting foreground moving objects from video sequences is an important task and also a hot topic in computer vision and image processing. Segmentation results can be used in many object-based video applications such as object-based video coding, content-based video retrieval, intelligent video surveillance and video-based human–computer interaction. In this paper, we present a novel moving object detection method based on improved VIBE and graph cut method from monocular video sequences. Firstly, perform moving object detection for the current frame based on improved VIBE method to extract the background and foreground information; then obtain the clusters of foreground and background respectively using mean shift clustering on the background and foreground information; Third, initialize the S/T Network with corresponding image pixels as nodes (except S/T node); calculate the data and smoothness term of graph; finally, use max flow/minimum cut to segmentation S/T network to extract the motion objects. Experimental results on indoor and outdoor videos demonstrate the efficiency of our proposed method. 相似文献
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Video cameras have been widely installed in public facilities for the surveillance applications. So, video authentication has becoming increasingly attractive. This paper presents a dual watermarking for video authentication based on moving objects. For each frame, the frame index, as a watermark is first embedded into the moving objects of the corresponding frame using a reversible watermarking method, aiming to detect the temporal tampering. Then the principle content and the details of the moving objects combined with the authentication code, as the other watermark, are embedded into the frame for spatial tampering location and recovery. Specially, a synthesized frame method is proposed for lossless recovery of moving objects and effective extraction of frame index. Statistical analysis and experiment results show that the proposed method can locate spatial, temporal and spatio-temporal tampering accurately. The spatial tampered regions can be recovered approximately and the moving objects can be restored completely when the tampered area is limited. 相似文献
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提出了时间空间频率混合领域中基于一维FFT频谱估计的移动物体速度估计方法。结合移动物体的抽出,把具有两个移动物体的序列图像分离为分别包含一个移动物体的两个序列图像,当再次进行速度估计时会大大减少背景及噪声对速度估计结果的影响,可提高速度估计的精度。 相似文献