Multiple motion scene reconstruction with uncalibrated cameras

In this paper, we describe a reconstruction method for multiple motion scenes, which are scenes containing multiple moving objects, from uncalibrated views. Assuming that the objects are moving with constant velocities, the method recovers the scene structure, the trajectories of the moving objects, the camera motion, and the camera intrinsic parameters (except skews) simultaneously. We focus on the case where the cameras have unknown and varying focal lengths while the other intrinsic parameters are known. The number of the moving objects is automatically detected without prior motion segmentation. The method is based on a unified geometrical representation of the static scene and the moving objects. It first performs a projective reconstruction using a bilinear factorization algorithm and, then, converts the projective solution to a Euclidean one by enforcing metric constraints. Experimental results on synthetic and real images are presented.     

动态场景图像序列中运动目标检测新方法

在动态场景图像序列中检测运动目标时,如何消除因摄影机运动带来的图像帧间全局运动的影响,以便分割图像中的静止背景和运动物体,是一个必须解决的难题。针对复杂背景下动态场景图像序列的特性,给出了一种新的基于场景图像参考点3D位置恢复的图像背景判别方法和运动目标检测方法。首先,介绍了图像序列的层次化运动模型以及基于它的运动分割方法;然后,利用估计出的投影矩阵计算序列图像中各运动层的参考点3D位置,根据同一景物在不同帧中参考点3D位置恢复值的变化特性,来判别静止背景对应的运动层和运动目标对应的运动层,从而分割出图像中的静止背景和运动目标;最后,给出了动态场景图像序列中运动目标检测的详细算法。实验结果表明,新算法较好地解决了在具有多组帧间全局运动参数的动态场景序列图像中检测运动目标的问题,较大地提高了运动目标跟踪算法的有效性和鲁棒性。     

Adaptive Grid Refinement Procedures for Efficient Optical Flow Computation

Two approaches are described that improve the efficiency of optical flow computation without incurring loss of accuracy. The first approach segments images into regions of moving objects. The method is based on a previously defined Galerkin finite element method on a triangular mesh combined with a multiresolution segmentation approach for object flow computation. Images are automatically segmented into subdomains of moving objects by an algorithm that employs a hierarchy of mesh coarseness for the flow computation, and these subdomains are reconstructed over a finer mesh on which to recompute flow more accurately. The second approach uses an adaptive mesh in which the resolution increases where motion is found to occur. Optical flow is computed over a reasonably coarse mesh, and this is used to construct an optimal adaptive mesh in a way that is different from the gradient methods reported in the literature. The finite element mesh facilitates a reduction in computational effort by enabling processing to focus on particular objects of interest in a scene (i.e. those areas where motion is detected). The proposed methods were tested on real and synthetic image sequences, and promising results are reported.     

Efficient region-based motion segmentation for a video monitoring system

This paper presents an efficient region-based motion segmentation method for segmentation of moving objects in a traffic scene with a focus on a video monitoring system (VMS). The presented method consists of two phases: first, in the motion detection phase, the positions of moving objects in a scene are determined using an adaptive thresholding method. To detect varying regions by moving objects, instead of determining the threshold value manually, we use an adaptive thresholding method to automatically choose the threshold value. Second, in the motion segmentation phase, pixels that have similar intensity and motion information are segmented using a weighted k-means clustering algorithm to the binary region of the motion mask obtained in the motion detection. In this way, we need not process a whole image so computation time is reduced. Experimental results demonstrate robustness not only in the variation of luminance conditions and changes in environmental conditions, but also for occlusions among multiple moving objects.     

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.     

一种用于动态场景的全景表示方法

针对全景图无法表示动态场景这一问题，提出一种用于动态场景的全景图表示方法，将视频纹理和全景图结合起来，构造动态全景图。系统首先将一系列定点拍摄的图像拼接成全景图，然后用摄像机拍摄场景中周期或随机运动的物体，提取视频纹理，最后视频纹理与全景图对准并融合，生成动态全景图。动态全景图既保持静态全景图全视角漫游的优点，又使得场景具有动态的特征，极大地增强漫游的真实感。     

Motion segmentation using occlusions

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).     

Collision Detection between Robot Arms and People

As the result of an increasing number of robots performing tasks in a range of human life activites, human–robot interaction has become a very active research field. Safety of people around robots is a major concern. This paper presents some research in this context: our aim is to avoid mechanical injure of people interacting with robots. We approach the collision detection problem in a scene with people and several moving robot arms. Fast collision detection for practical motion planning depends on an adequate spatial representation for the objects involved in the scene. The authors have previosly proposed a system that automatically generates a hierarchy of approximations for general objects. The spatial model has interesting properties and has been used in efficient collision detection algorithms between moving robots [8]. In spatial representations, there is a trade-off between generality and efficiency. Some existing approaches claim to be general but they are less efficient. In this paper, we present two extensions to the spatial model. First, the system can deal with a general class of objects, those that are composed of nonhomogeneous generalized cylinders. Secondly, a simple method for automatic converting from a polyhedral representation to such a generalized cylinder is presented. Therefore, we enhance the generality of the system but without compromising the efficiency. With these extensions virtually any object can be dealt with, and particularly those composing the human body.     

Unsupervised motion detection with background update and shadow suppression

An algorithm is developed to detect moving object and suppress shadow.According to motion variations caused by some moving objects in a scene,a background update approach is proposed.The developed update method efficiently prevents undesired corruption of background and does not consider the adaptation coefficient or the learning rate used in some existing algorithms.A multi-scale wavelet transform methodology is used to segment foreground from a clutter background.The optimal selection of threshold value is automatically determined which does not require any complex supervised training or manual calibration.According to photometric invariant,a color ratio difference is proposed to suppress shadow.Some complete foreground motion object regions are extracted by integrating moving object segmentation in the multi-scale wavelet with shadow suppression in the color ratio difference.The mentioned method is less affected by the presence of moving objects in a scene.Experimental results show that the proposed approach is efficient in detecting motion objects and suppressing shadows by comparisons.