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1.
一种用于机器人的物体运动参数快速识别方法 总被引:5,自引:0,他引:5
为了使机器人能跟踪并抓取运动目标,实时给出目标物的运动参数是首要问题,也是个困
难的问题.本文给出一种物体二维运动的快速估计算法,不需要抽取物体特征,也不需要事先
知道物体的模型,而是通过运动物体序列图像的复数矩与运动参数之间的关系,来恢复物体的
二维运动.该算法与基于傅里叶描述子的运动估计方法进行了比较,证明了算法的快速性和
准确性. 相似文献
2.
针对背景运动时的运动目标分割问题,提出了一种对视频序列中的多个运动目标进行分割和跟踪的新方法。该方法着眼于运动的且较为复杂的背景,首先利用光流约束方程和背景运动模型建立一个基于时空域的能量函数,然后用该函数进行背景运动速度的估算和运动目标的分割和跟踪。而时空域中的运动目标的最佳分割,乃是通过使该能量函数最小化来驱动时空曲面演化实现。时空曲面的演化采用了水平集PDEs(Partial Differential Equations)方法。实验中,用实际的图像序列验证了该算法及其数值实现。实验表明,该方法能够同时进行背景运动速度的估算、运动目标的分割和跟踪。 相似文献
3.
Joint segmentation of collectively moving objects using a bag-of-words model and level set evolution
In scenes with collectively moving objects, to disregard the individual objects and take the entire group into consideration for motion characterization is a promising approach with wide application prospects. In contrast to studies on the segmentation of independently moving objects, our purpose is to construct a segmentation of these objects to characterize their motions at a macroscopic level. In general, the collectively moving objects in a group have very similar motion behavior with their neighbors and appear as a kind of global collective motion. This paper presents a joint segmentation approach for these collectively moving objects. In our model, we extract these macroscopic movement patterns based on optical flow field sequences. Specifically, a group of collectively moving objects correspond to a region where the optical flow field has high magnitude and high local direction coherence. As a result, our problem can be addressed by identifying these coherent optical flow field regions. The segmentation is performed through the minimization of a variational energy functional derived from the Bayes classification rule. Specifically, we use a bag-of-words model to generate a codebook as a collection of prototypical optical flow patterns, and the class-conditional probability density functions for different regions are determined based on these patterns. Finally, the minimization of our proposed energy functional results in the gradient descent evolution of segmentation boundaries which are implicitly represented through level sets. The application of our proposed approach is to segment and track multiple groups of collectively moving objects in a large variety of real-world scenes. 相似文献
4.
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
5.
6.
基于图像的动目标检测技术 总被引:1,自引:0,他引:1
本文研究了光流估计的基本算法,从运动分割的角度的提出了使用光流分割方案来解决复杂背景下的动目标检测问题。文中分析了标准光流估计的缺陷及其原因,并使用鲁棒性估计技术提高了光流估计算法的实用性。最后给出了动目标检测算法的仿真结果,表明利用图像的运动特征来实现目标检测是一种很有效的方法。 相似文献
7.
设计了一种基于可靠特征集合匹配的内容完整的视频稳定算法。为了避免运动前景上的特征点参与运动估计,由经典的KLT(Kanade-Lucas-Tomasi)算法提取特征点,而后基于特征有效性判定规则对特征点集合进行有效性验证以提高特征点的可靠性。利用通过验证的特征点对全局运动进行估计,得到精确的运动参数并据此对视频图像进行运动补偿。对于运动补偿造成的无定义区,首先计算当前帧的定义区与相邻帧的光流,以此为向导腐蚀无定义区;利用拼接的方法,填充仍为无定义区的像素。实验结果表明该算法对于前景物体运动具有较好的鲁棒性并能够生成内容完整的稳定视频序列。 相似文献
8.
目的:在彩色图像分割中,光流法能够得到运动区域,但难以获得运动目标准确的分割边界,而常用的算法往往会产生过分割。为了克服光流法的不足,在保留显著性区域的同时抑制过分割,从而获得具有运动一致性区域的分割结果,提出融合多特征的运动一致性图像分割算法。方法:首先通过Mean Shift算法获取图像的初始分割,然后利用空域信息(包括颜色、边缘和区域面积)对视觉感知上具有相似性的区域进行合并,再利用时域信息进行运动一致性区域合并,最终得到分割结果。结果:实验结果表明通过结合时空信息,该方法能够有效抑制过分割,不仅弥补了光流场不能准确提取目标边缘的不足,而且提高了分割目标的完整性。结论:与两种流行的彩色图像分割算法相比,所提方法获得了更加理想的结果。 相似文献
9.
Weber J. Malik J. 《IEEE transactions on pattern analysis and machine intelligence》1997,19(2):139-143
We present an algorithm for identifying and tracking independently moving rigid objects from optical flow. Some previous attempts at segmentation via optical flow have focused on finding discontinuities in the flow field. While discontinuities do indicate a change in scene depth, they do not in general signal a boundary between two separate objects. The proposed method uses the fact that each independently moving object has a unique epipolar constraint associated with its motion. Thus motion discontinuities based on self-occlusion can be distinguished from those due to separate objects. The use of epipolar geometry allows for the determination of individual motion parameters for each object as well as the recovery of relative depth for each point on the object. The algorithm assumes an affine camera where perspective effects are limited to changes in overall scale. No camera calibration parameters are required. A Kalman filter based approach is used for tracking motion parameters with time 相似文献
10.
Chin-Hung Teng Shang-Hong Lai Yung-Sheng Chen Wen-Hsing Hsu 《Computer Vision and Image Understanding》2005,97(3):315-346
In this paper, we present a very accurate algorithm for computing optical flow with non-uniform brightness variations. The proposed algorithm is based on a generalized dynamic image model (GDIM) in conjunction with a regularization framework to cope with the problem of non-uniform brightness variations. To alleviate flow constraint errors due to image aliasing and noise, we employ a reweighted least-squares method to suppress unreliable flow constraints, thus leading to robust estimation of optical flow. In addition, a dynamic smoothness adjustment scheme is proposed to efficiently suppress the smoothness constraint in the vicinity of the motion and brightness variation discontinuities, thereby preserving motion boundaries. We also employ a constraint refinement scheme, which aims at reducing the approximation errors in the first-order differential flow equation, to refine the optical flow estimation especially for large image motions. To efficiently minimize the resulting energy function for optical flow computation, we utilize an incomplete Cholesky preconditioned conjugate gradient algorithm to solve the large linear system. Experimental results on some synthetic and real image sequences show that the proposed algorithm compares favorably to most existing techniques reported in literature in terms of accuracy in optical flow computation with 100% density. 相似文献
11.
Motion segmentation using occlusions 总被引:4,自引:0,他引:4
Ogale AS Fermüller C Aloimonos Y 《IEEE transactions on pattern analysis and machine intelligence》2005,27(6):988-992
We examine the key role of occlusions in finding independently moving objects instantaneously in a video obtained by a moving camera with a restricted field of view. In this problem, the image motion is caused by the combined effect of camera motion (egomotion), structure (depth), and the independent motion of scene entities. For a camera with a restricted field of view undergoing a small motion between frames, there exists, in general, a set of 3D camera motions compatible with the observed flow field even if only a small amount of noise is present, leading to ambiguous 3D motion estimates. If separable sets of solutions exist, motion-based clustering can detect one category of moving objects. Even if a single inseparable set of solutions is found, we show that occlusion information can be used to find ordinal depth, which is critical in identifying a new class of moving objects. In order to find ordinal depth, occlusions must not only be known, but they must also be filled (grouped) with optical flow from neighboring regions. We present a novel algorithm for filling occlusions and deducing ordinal depth under general circumstances. Finally, we describe another category of moving objects which is detected using cardinal comparisons between structure from motion and structure estimates from another source (e.g., stereo). 相似文献
12.
基于假设检验和区域合并的视频对象分割 总被引:4,自引:0,他引:4
提出了基于假设检验和区域合并的视频对象分割算法。初始分割采用分水岭算法,接着根据颜色相似性进行区域合并,然后利用光流场估计和全局运动估计获得全局运动的残余误差,最后以各个区域的残余误差数据进行假设检验和小区域验证来确定运动区域,通过组合所有的运动区域即可分割出具有准确边缘的完整视频对象。对MPEG-4测试序列的实验结果表明了本算法具有良好的分割性能。 相似文献
13.
Detecting and estimating motions of fast moving objects has many important applications. However, most existing motion estimation
techniques have difficulties in handling large motions in the scene. In this paper, we extend our recently proposed reliability-based
stereo vision technique to solving large motion estimation problem. Compared with our stereo vision approach, the new algorithm
removes the constant penalty assumption and explicitly enforces the inter-scanline consistency constraint. The resulting algorithm
can handle sequences that contain large motions and can produce optical flows with 100% density over the entire image domain.
The experimental results indicate that it can generate more accurate optical flows than existing approaches. 相似文献
14.
一种基于帧间差分和光流技术结合的运动车辆检测和跟踪新算法 总被引:1,自引:0,他引:1
为了改进智能交通中的运动车辆检测和跟踪方法,提出一种基于改进的帧间差分和光流技术结合的运动车辆检测和跟踪的新方法。先用帧间差分法检测出运动物体的运动区域,再计算差值图中不为零处的光流,然后利用其光流场来实现运动目标的跟踪。为了减少计算量,提出一种基于最优估计的点匹配技术和光流均匀采样策略的光流场计算方法,并通过对灰度化后的光流场进行自适应阈值分割、形态学滤波等处理,实现了实时的运动目标检测和跟踪。 相似文献
15.
Heitz F. Bouthemy P. 《IEEE transactions on pattern analysis and machine intelligence》1993,15(12):1217-1232
The estimation of dense velocity fields from image sequences is basically an ill-posed problem, primarily because the data only partially constrain the solution. It is rendered especially difficult by the presence of motion boundaries and occlusion regions which are not taken into account by standard regularization approaches. In this paper, the authors present a multimodal approach to the problem of motion estimation in which the computation of visual motion is based on several complementary constraints. It is shown that multiple constraints can provide more accurate flow estimation in a wide range of circumstances. The theoretical framework relies on Bayesian estimation associated with global statistical models, namely, Markov random fields. The constraints introduced here aim to address the following issues: optical flow estimation while preserving motion boundaries, processing of occlusion regions, fusion between gradient and feature-based motion constraint equations. Deterministic relaxation algorithms are used to merge information and to provide a solution to the maximum a posteriori estimation of the unknown dense motion field. The algorithm is well suited to a multiresolution implementation which brings an appreciable speed-up as well as a significant improvement of estimation when large displacements are present in the scene. Experiments on synthetic and real world image sequences are reported 相似文献
16.
Huang Y. Palaniappan K. Zhuang X. Cavanaugh J.E. 《IEEE transactions on pattern analysis and machine intelligence》1995,17(12):1177-1190
Optic flow motion analysis represents an important family of visual information processing techniques in computer vision. Segmenting an optic flow field into coherent motion groups and estimating each underlying motion is a very challenging task when the optic flow field is projected from a scene of several independently moving objects. The problem is further complicated if the optic flow data are noisy and partially incorrect. In this paper, the authors present a novel framework for determining such optic flow fields by combining the conventional robust estimation with a modified genetic algorithm. The baseline model used in the development is a linear optic flow motion algorithm due to its computational simplicity. The statistical properties of the generalized linear regression (GLR) model are thoroughly explored and the sensitivity of the motion estimates toward data noise is quantitatively established. Conventional robust estimators are then incorporated into the linear regression model to suppress a small percentage of gross data errors or outliers. However, segmenting an optic flow field consisting of a large portion of incorrect data or multiple motion groups requires a very high robustness that is unattainable by the conventional robust estimators. To solve this problem, the authors propose a genetic partitioning algorithm that elegantly combines the robust estimation with the genetic algorithm by a bridging genetic operator called self-adaptation 相似文献
17.
We propose a new algorithm for dense optical flow computation. Dense optical flow schemes are challenged by the presence of
motion discontinuities. In state of the art optical flow methods, over-smoothing of flow discontinuities accounts for most
of the error. A breakthrough in the performance of optical flow computation has recently been achieved by Brox et~al. Our
algorithm embeds their functional within a two phase active contour segmentation framework. Piecewise-smooth flow fields are
accommodated and flow boundaries are crisp. Experimental results show the superiority of our algorithm with respect to alternative
techniques. We also study a special case of optical flow computation, in which the camera is static. In this case we utilize
a known background image to separate the moving elements in the sequence from the static elements. Tests with challenging
real world sequences demonstrate the performance gains made possible by incorporating the static camera assumption in our
algorithm. 相似文献
18.
In this paper, we present a new background estimation algorithm which effectively represents both background and foreground. The problem is formulated with a labeling problem over a patch-based Markov random field (MRF) and solved with a graph-cuts algorithm. Our method is applied to the problem of mosaic blending considering the moving objects and exposure variations of rotating and zooming camera. Also, to reduce seams in the estimated boundaries, we propose a simple exposure correction algorithm using intensities near the estimated boundaries. 相似文献
19.
Mitiche A Sekkati H 《IEEE transactions on pattern analysis and machine intelligence》2006,28(11):1818-1829
This study investigates a variational, active curve evolution method for dense three-dimensional (3D) segmentation and interpretation of optical flow in an image sequence of a scene containing moving rigid objects viewed by a possibly moving camera. This method jointly performs 3D motion segmentation, 3D interpretation (recovery of 3D structure and motion), and optical flow estimation. The objective functional contains two data terms for each segmentation region, one based on the motion-only equation which relates the essential parameters of 3D rigid body motion to optical flow, and the other on the Horn and Schunck optical flow constraint. It also contains two regularization terms for each region, one for optical flow, the other for the region boundary. The necessary conditions for a minimum of the functional result in concurrent 3D-motion segmentation, by active curve evolution via level sets, and linear estimation of each region essential parameters and optical flow. Subsequently, the screw of 3D motion and regularized relative depth are recovered analytically for each region from the estimated essential parameters and optical flow. Examples are provided which verify the method and its implementation 相似文献