基于序列图像的运动目标检测新算法

基于SURF帧间配准和运动历史图像构建,提出了一种基于序列图像的运动目标检测新方法。针对运动检测容易出现虚警的情况,结合多帧关联思想和碰撞检测理论提出多帧碰撞检测算法,以剔除虚假运动目标。在标准测试集上测试序列仿真验证,结果表明,该运动目标检测算法具备较高的查全率以及极高的检测精度和极低的虚警率。     

基于扩展卡尔曼滤波的空间小目标跟踪算法

由于天基平台拍摄天空图片时,背景和相机同时发生相对运动,造成相邻帧之间无法通过简单的帧差法得到运动的小目标,造成了空间目标检测的难度。在分析星空图像模型的基础上,提出了一种提取特征点组成匹配三角形的图像配准方法,该方法通过最优阈值的选取对单帧图像进行分割,去除背景噪声。将星点按面积大小划分,符合条件的星点组成特征三角形并在相邻帧中进行匹配得到运动参数。在配准时为了减小计算量,忽略背景插值只针对星点坐标矩阵进行处理。最后通过多帧轨迹关联检测出目标的运动轨迹。仿真实验表明,在运动的序列图像中,该方法能实现高检测率和低虚警率的实时检测。     

基于三角形匹配的空间小目标检测算法

由于天基平台拍摄天空图片时,背景和相机同时发生相对运动,造成相邻帧之间无法通过简单的帧差法得到运动的小目标,造成了空间目标检测的难度。在分析星空图像模型的基础上,提出了一种提取特征点组成匹配三角形的图像配准方法。首先对图像进行预处理,通过最优阈值的选取对单帧图像进行分割,去除背景噪声。将星点按面积大小划分,对符合条件的星点组成特征三角形并在相邻帧中进行匹配得到运动参数。在配准时为了减小计算量,忽略背景插值只针对星点坐标矩阵进行处理。最后通过多帧轨迹关联检测出目标的运动轨迹。仿真实验表明,在运动的序列图像中,该方法能实现高检测率和低虚警率的实时检测。     

从移动背景红外序列图像中检测运动目标

该文提出了一种从背景移动红外图像中自动检测运动目标的算法。该算法首先采用图像灰度互相关度量的匹配算法对连续的6帧序列进行配准,用第1帧和第4帧配准,第2帧和第5帧配准,第3帧和第6帧配准,然后用配准后的图像对分别做差分运算,再将3个差分图像按像素相乘,在运动目标处得到了非常尖锐的相关峰。这为进一步自动跟踪目标提供了一个跟踪窗口的中心点。实验结果验证了该方法的有效性。     

基于最大值投影和快速配准的空间小目标检测

天基观测平台下弱小目标的检测是分析空间安全的重要研究内容。由于空间中存在大量外观与目标相似的恒星导致可利用空间分布信息缺乏;观测平台的不规则性运动导致帧间成像差异,都使得开发自动快速处理算法的难度增加。在分析星空图像模型的基础上,提出了一种基于三角形匹配和最大值投影的小目标检测方法。首先通过特征三角形对序列图像进行配准,并采用星点坐标矩阵的方法减小计算量。然后针对序列帧所有图像,采用最大值投影变换的方法,检测运动的小目标。最后通过200帧观测图像对算法进行验证,实验表明该方法能实时、准确地对目标进行检测,同时可以精确地定位目标质心。     

野外复杂背景下红外图像的目标检测

野外复杂背景下红外图像序列目标检测是红外野外监视中的重点问题。大量的背景物增加了目标检测的难度。文中针对野外复杂背景下红外图像序列的特点,提出了一种实用的运动目标检测算法。该算法包括两个处理步骤：首先,在场景配准后利用帧间差图像提取目标的运动信息,并据此进行目标的粗检测;其次,合目标运动在时间和空间上的相关性进行精检测。粗检测的低漏判度和精检测的低误差率保证了算法的可靠性。在检测的同时算法确定了目     

视频中运动物体检测算法的研究

为了从运动场景中(视频序列)提取高质量的运动物体,本文提出一种基于代数多重网格和帧间差分法相结合的运动目标检测算法,该方法利用代数多重网格对帧序列图像进行重构,分别得到重建的三层图像,再利用帧差法对处理过的相邻图像进行差分处理,从而提取场景中运动的物体,实验证明,此方法解决了帧间差分法不能提取对象完整区域和对场景噪声等敏感的问题,鲁棒性高,能准确的检测出在场景中运动的目标。     

基于矩阵恢复的红外偏振图像分区配准算法

由于红外偏振图像具有灰度分布差别大、特征信息不明显等特点,传统区域或特征的配准算法精度难以满足红外偏振信息解析要求。从图像间相似性出发,以矩阵秩作为图像相似性的度量,提出了一种基于矩阵恢复的红外偏振图像分区配准算法。将一组待配准图像组成变化矩阵,并分解成低秩和稀疏两部分。以低秩变换矩阵核范数与稀疏变换矩阵1范数的和为目标函数,利用增广拉格朗日乘子法求得目标函数值最小时的各区域变换参数,加权平均后得到图像组的配准结果。实验结果表明,该算法配准变换参数误差小于0.02 pixel,且对噪声不敏感。     

低秩稀疏分解下多尺度积的运动目标检测方法

针对基于矩阵分解的运动目标检测方法易受自然场景中背景的小幅抖动和摄像头抖动等因素影响的问题,提出了一种利用多尺度积的低秩稀疏矩阵分解算法。算法假设,静态背景视频序列中,每帧图像背景可近似视为处于同一低秩子空间中,图像前景则可视为偏离低秩空间的残差部分。首先对图像序列进行滤波、仿射变换等预处理得到视频序列观测数据矩阵;然后对数据矩阵进行低秩稀疏分解得到序列图像的低秩背景部分和每帧图像的稀疏前景部分;最后对稀疏前景部分采用小波变换模极大值与多尺度积方法检测目标边缘,并进行形态学处理,得到准确的运动目标。实验结果表明,算法检测到的运动目标清晰、完整,能有效地处理光照变化、摄像头小幅度抖动、图像背景局部小幅度变化等情况下的运动目标检测。      

复杂背景下运动目标检测方法

利用相邻两帧图像的运动信息更新特征块提取阈值,实时地提取出当前帧图像中最能体现背景运动特征的特征块。采用块匹配的运动估计方法获取了各块的运动矢量,再依据所有特征块运动矢量的统计特性提取出背景运动矢量来配准差分,实现了目标的检测。实验结果表明,此方法能有效地检测复杂背景下的运动目标。     

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.     

频繁抖动相机下的运动目标快速检测算法

为了区分相机抖动导致的图像运动与真实目标导致的图像运动,提出一种新的算法,其分析了两类运动分布,而不是亮度/彩色分布.一类运动分布是长时间范围建立起来的,其仅包含背景运动.另一类运动分布是短时间范围建立起来的,既包含背景运动也包含目标运动.最终通过对这两类运动分布的差分进行阈值分割实现运动目标检测.该算法运行速度快,且内存开销低,实验表明其在有着频繁相机抖动的室内及室外场景中表现出优异的性能.     

Spatiotemporal motion boundary detection and motion boundary velocity estimation for tracking moving objects with a moving camera: a level sets PDEs approach with concurrent camera motion compensation

The purpose of this study is to investigate a method of tracking moving objects with a moving camera. This method estimates simultaneously the motion induced by camera movement. The problem is formulated as a Bayesian motion-based partitioning problem in the spatiotemporal domain of the image quence. An energy functional is derived from the Bayesian formulation. The Euler-Lagrange descent equations determine imultaneously an estimate of the image motion field induced by camera motion and an estimate of the spatiotemporal motion undary surface. The Euler-Lagrange equation corresponding to the surface is expressed as a level-set partial differential equation for topology independence and numerically stable implementation. The method can be initialized simply and can track multiple objects with nonsimultaneous motions. Velocities on motion boundaries can be estimated from geometrical properties of the motion boundary. Several examples of experimental verification are given using synthetic and real-image sequences.     

雷达表盘图像移动目标的快速识别与跟踪

针对以往对机载雷达表盘的图像移动目标的识别与跟踪速度较慢,缺乏实时性的问题,提出一种快速识别和跟踪移动目标的方法。分析和总结了关于移动物体连续两帧图像的特点,并根据它们在图像对比中的空间位移上的相对变化关系,运用OTSU算法、差影法、直方图统计特征分析法,对移动目标进行识别与跟踪。该方法处理一帧图像的时间不超过40ms,具有识别速度快、跟踪准确、抗干扰能力强等特点。     

A hybrid algorithm for automatic segmentation of slowly moving objects

Segmentation of moving objects in video sequences is a basic task in many applications. However, it is still challenging due to the semantic gap between the low-level visual features and the high-level human interpretation of video semantics. Compared with segmentation of fast moving objects, accurate and perceptually consistent segmentation of slowly moving objects is more difficult. In this paper, a novel hybrid algorithm is proposed for segmentation of slowly moving objects in video sequence aiming to acquire perceptually consistent results. Firstly, the temporal information of the differences among multiple frames is employed to detect initial moving regions. Then, the Gaussian mixture model (GMM) is employed and an improved expectation maximization (EM) algorithm is introduced to segment a spatial image into homogeneous regions. Finally, the results of motion detection and spatial segmentation are fused to extract final moving objects. Experiments are conducted and provide convincing results.     

A fast valley-based segmentation for detection of slowly moving objects

Moving object detection in a video sequence is the first and most important step in many computer vision applications. However, it is challenging for a machine to match with the human visual perception level. Motion information of slowly moving object is highly erroneous in comparison with fast moving object. Therefore, in real time, accurate segmentation of slowly moving objects is more challenging. In this paper, a fast and efficient segmentation algorithm is proposed for the detection of slowly moving object in a video sequence. The proposed method has three steps to extract the slowly moving object in a video. In the first step, an averaging frame difference method is proposed to extract the motion information. In the second step, a valley-based thresholding is proposed to segment all the frames of a video. In the final step, the motion information and spatial homogeneous region information are merged to extract the slowly moving object.     

An automatic segmentation method for moving objects based on the spatial-temporal information of video

The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find pre~：ise object boundaries of moving objects. To locate moving objects in image sequences, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.     

Fast frame-to-frame interpolation technique for scenes containing moving objects

A fast scanline algorithm for a raster-scan graphics display is proposed based on an observation that a sequence of successive image frames in animation mostly consists of still objects with relatively few moving objects. In the proposed algorithm, successive images within a given time interval are generated using an interpolation of two images generated at the start and end time of the given time interval. Experimental results have shown that a speedup factor of 3.2-12.9, depending on the percentage of the moving objects among all objects, is obtained using the algorithm, compared to the conventional scheme that does not use the frame-to-frame image coherence.<>     

Bayesian algorithms for simultaneous structure from motion estimation of multiple independently moving objects.

In this paper, the problem of simultaneous structure from motion estimation for multiple independently moving objects from a monocular image sequence is addressed. Two Bayesian algorithms are presented for solving this problem using the sequential importance sampling (SIS) technique. The empirical posterior distribution of object motion and feature separation parameters is approximated by weighted samples. The first algorithm addresses the problem when only two moving objects are present. A singular value decomposition (SVD)-based sample clustering algorithm is shown to be capable of separating samples related to different objects. A pair of SIS procedures is used to track the posterior distribution of the motion parameters. In the second algorithm, a balancing step is added into the SIS procedure to preserve samples of low weights so that all objects have enough samples to propagate empirical motion distributions. By using the proposed algorithms, the relative motions of all the moving objects with respect to the camera can be simultaneously estimated. Both algorithms have been tested on synthetic and real-image sequences. Improved results have been achieved.     

矿井车载摄像系统中电子稳像算法的研究

为克服矿井车载摄像系统在视频采集过程中的图像抖动问题,提出了一种基于MIC-Harris角点特征的自适应运动补偿电子稳像算法。首先,提出MIC-Harris角点检测算法和由粗到精的匹配策略,实现序列中特征点的快速精确提取。然后根据矿井移动车载图像运动特点,构建运动参数估计模型,运用最小二乘法实现全局运动矢量的求解。最后,提出自适应相邻帧补偿方法,结合Kalman滤波实现补偿矢量的计算,在滤除高频随机抖动同时保留了车载摄像机低频主动运动,并克服了连续视频序列长时间稳像处理的帧间累积误差问题。实验仿真结果表明：本文提出的算法不仅融合了MIC运算速度快和Harris准确度高的优点,而且能够实现矿井车载摄像系统的长时间连续稳像处理,具有较好的稳像效果。