Simultaneous egomotion estimation,segmentation, and moving object detection

Robust egomotion estimation is a key prerequisite for making a robot truly autonomous. In previous work, a multimodel extension of random sample consensus (RANSAC) was introduced to deal with environments with rapid changes by incorporating moving object information. A multiscale matching algorithm was also proposed to resolve the issue of imperfect segmentation. In this paper, we present a novel specialization of RANSAC that extends the previous work. A unified framework is introduced to achieve simultaneously egomotion estimation, multiscale segmentation, and moving object detection in the RANSAC paradigm. The motivation of this work is to provide a robust real‐time solution to the problem of egomotion estimation, segmentation, and moving object detection in highly dynamic environments. The idea is to augment the discriminative power of spatial and temporal appearances of objects by the spatiotemporal consistency. The objective is twofold. First, split mismerged segments and distinguish nonstationary objects from stationary objects by the spatial consistency. Second, merge oversegmented segments and differentiate moving objects from outlying objects by the temporal consistency. Moving objects of considerably different sizes, from pedestrians to trucks, can be properly segmented and correctly detected. We also show that the performance of egomotion estimation can be further improved by taking into account both stationary and moving object information. Our approach is extensively evaluated on challenging data sets and compared to the state of the art. The experiments also show that our approach serves as a general framework that works well with various planar range data. © 2011 Wiley Periodicals, Inc.     

实时运动目标检测与跟踪平台的构建*

构建了一个基于图像采集卡的复杂环境下实时运动目标检测与跟踪的实验平台。基于此平台提出并实现了一种改进的运动目标检测算法,它融合了帧间差分法和背景差分法的优点,以适应复杂环境的变化。实验表明,该算法利用所构建的平台,对变化场景中的运动目标实施了快速有效的检测与跟踪,为智能视频技术的研究提供了一个实用的实验平台。     

基于运动显著性的移动镜头下的运动目标检测

针对移动镜头下的运动目标检测中的背景建模复杂、计算量大等问题,提出一种基于运动显著性的移动镜头下的运动目标检测方法,在避免复杂的背景建模的同时实现准确的运动目标检测。该方法通过模拟人类视觉系统的注意机制,分析相机平动时场景中背景和前景的运动特点,计算视频场景的显著性,实现动态场景中运动目标检测。首先,采用光流法提取目标的运动特征,用二维高斯卷积方法抑制背景的运动纹理;然后采用直方图统计衡量运动特征的全局显著性,根据得到的运动显著图提取前景与背景的颜色信息;最后,结合贝叶斯方法对运动显著图进行处理,得到显著运动目标。通用数据库视频上的实验结果表明,所提方法能够在抑制背景运动噪声的同时,突出并准确地检测出场景中的运动目标。     

基于颜色分割和LBP纹理检测的运动目标提取

针对传统LBP纹理检测在运动目标提取中对原地或缓慢运动物体容易误判为背景的问题,提出一种基于颜色分割和LBP纹理检测的提取方法。主要思想分为3步：根据LBP纹理检测结果映射得到宏块级粗糙运动目标;根据K‐means颜色分割方法得到颜色分类信息;根据提出的宏块交叠机制,将颜色信息与运动信息进行融合,得到最终的提取结果。对比实验结果表明,该方法在保持良好的光照鲁棒性和阴影抵抗力的同时,可以有效改善运动目标空洞、背景融入等问题,满足实时要求。     

Querying imprecise data in moving object environments

In moving object environments, it is infeasible for the database tracking the movement of objects to store the exact locations of objects at all times. Typically, the location of an object is known with certainty only at the time of the update. The uncertainty in its location increases until the next update. In this environment, it is possible for queries to produce incorrect results based upon old data. However, if the degree of uncertainty is controlled, then the error of the answers to queries can be reduced. More generally, query answers can be augmented with probabilistic estimates of the validity of the answer. We study the execution of probabilistic range and nearest-neighbor queries. The imprecision in answers to queries is an inherent property of these applications due to uncertainty in data, unlike the techniques for approximate nearest-neighbor processing that trade accuracy for performance. Algorithms for computing these queries are presented for a generic object movement model and detailed solutions are discussed for two common models of uncertainty in moving object databases. We study the performance of these queries through extensive simulations.     

Change detection for moving object segmentation with robust background construction under Wronskian framework

Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static background, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The results obtained by the proposed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques.     

推广GAC模型在运动目标分割中的应用

针对视频序列运动目标的分割,研究了传统的运动目标检测算法和基于推广GAC模型的图像分割算法的优势和缺陷,并将二者进行系统的结合,由“粗”到“细”地实现了对运动目标边缘的精确分割。实验表明,算法简单有效,在保证目标分割实时性的前提下,发挥了推广GAC模型在目标分割中的优势。     

嵌入式系统中视频运动对象分割

提出了一种基于嵌入式系统的视频运动对象分割算法。首先利用差图像法抽取出运动的像素点,然后通过统计像素点的状态变化频率来区分运动物体和动态背景,并配合一权值状态矩阵将全局光照突变和动态背景像素自适应融合到背景中,从而分割出运动对象并进行跟踪。实验结果表明,该算法在嵌入式系统中实时跟踪运动目标取得了很好的效果。     

一种基于DA-STMRF模型的运动目标分割方法

为克服传统的时空马尔可夫随机场模型中全局一致平滑约束引起的过平滑,根据间断自适应的思想,结合边缘信息,提出了一种基于间断自适应时空马尔可夫随机场模型的运动目标分割方法。帧差图像二值化得到初始标记场,初始标记场进行“与”操作获得共同标记场,通过构造相应的能量函数,用Metroplis采样器算法实现共同标记场的优化。通过实验验证了该算法的有效性。     

河道监控视频的运动对象分割算法

针对内河航道监控视频的特点,提出一种基于背景差法的对象分割算法。首先在HSI颜色空间里利用像素的色调和亮度对其进行归类;然后利用基于块处理的方法确定背景像素,并在背景缓慢变化和急速变化时,采用定时和实时的背景重构方法进行背景更新;最后利用背景差提取运动对象。     

摄像机旋转运动下的快速目标检测算法

论文提出了一种摄像机旋转运动下的快速目标检测算法。首先为图像的全 局运动建立旋转参数模型,然后基于运动预测在相邻帧之间建立SIFT 特征点对,利用 RANSAC 去除外点的影响,结合最小二乘法求解全局运动参数进行运动补偿,基于残差图 像的更新策略实时更新特征点集,以适应背景的变化,最后使用帧差法获得运动目标。该算 法不仅保持了SIFT 本身的优越性能,而且极大地提高了检测速度。实验结果表明该算法可 以实时准确的检测出运动目标。     

基于多传感器融合的运动平台运动目标检测

基于运动平台的运动目标检测在计算机视觉等领域有着十分广阔的应用,基于单一视觉传感器平台目前很难满足实用要求。提出一种融合视觉传感器、微机电惯性传感器和距离传感器信息的运动平台运动目标检测新方法。利用惯性传感器获得的平台运动信息和距离传感器获得的场景深度信息,采用由粗到精的图像配准策略,消除背景运动影响。利用配准后的图像信息在扩展卡尔曼滤波框架下对惯性传感器信息进行修正,以达到长期稳定检测的目的。实验结果证明了方法的稳健性和有效性。     

视频监控中运动物体的检测与跟踪

针对固定摄像头下的交通监控场景,首先给出一种基于分块原理的背景重建算法,克服了平均法重建的背景图像模糊的缺点。然后用减背景方法检测运动物体,并利用数学形态学方法对得到原始前景点作处理,填补了运动物体内部的空洞,减少了噪声点,改善了检测性能。为适应背景的变化,对背景进行自适应更新,并且通过对Meanshift算法的改进提高了跟踪的准确性。实验结果表明,算法在有效检测到运动物体的同时能够快速准确地跟踪运动物体。     

Moving object detection in the encrypted domain

Multimedia Tools and Applications - The privacy-preserving moving object detection has drawn a lot of interest lately. Nevertheless, current approaches use Paillier’s scheme for encryption...     

Coupled object detection and tracking from static cameras and moving vehicles

We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. Our approach is formulated in an MDL hypothesis selection framework, which allows it to recover from mismatches and temporarily lost tracks. Building upon a multi-view/multi-category object detector, it localizes cars and pedestrians in the input images. The 2D object detections are converted to 3D observations, which are accumulated in a world coordinate frame. Trajectory analysis in a spacetime window yields physically plausible trajectory candidates. Tracking is achieved by performing model selection after every frame. At each time instant, our approach searches for the globally optimal set of spacetime trajectories which provides the best explanation for the current image and all evidence collected so far, while satisfying the constraints that no two objects may occupy the same physical space, nor explain the same image pixels at any time. Successful trajectory hypotheses are then fed back to guide object detection in future frames. The resulting approach can initialize automatically and track a large and varying number of objects from both static and moving cameras. We evaluate our approach on several challenging video sequences with both a surveillance-type scenario and a scenario where the input videos are taken from a moving vehicle.     

一种运动目标检测优化算法

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。     

一种快速的运动目标检测算法

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。     

Moving object detection using region tracking

For many vision-based systems, it is important to detect a moving object automatically. The region-based motion estimation method is popular for automatic moving object detection. The region-based method has several advantages in that it is robust to noise and variations in illumination. However, there is a critical problem in that there exists an occlusion problem which is caused by the movement of the object. The occlusion problem results in an incorrect motion estimation and faulty detection of moving objects. When there are occlusion regions, the motion vector is not correctly estimated. That is, a stationary background in the occluded region can be classified as a moving object.In order to overcome this occlusion problem, a new occlusion detection algorithm is proposed. The proposed occlusion detection algorithm is motivated by the assumption that the distribution of the error histogram of the occlusion region is different from that of the nonocclusion region. The proposed algorithm uses the mean and variance values to decide whether an occlusion has occurred in the region. Therefore, the proposed occlusion detection and motion estimation scheme detects the moving regions and estimates the new motion vector, while avoiding misdetection caused by the occlusion problem. The experimental results for several video sequences demonstrate the robustness of the proposed approach to the occlusion problem.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003