Fast moving object detection with non-stationary background

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.     

OPTICS聚类与目标区域概率模型的多运动目标跟踪

目的 针对多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题,本文提出了一种基于OPTICS聚类与目标区域概率模型的方法。方法 首先引入了Harris-Sift特征点检测,完成相邻帧特征点匹配,提高了特征点跟踪精度和鲁棒性;再根据各运动目标与背景运动向量不同这一点,引入了改进后的OPTICS加注算法,在构建的光流图上聚类,从而准确的分离出背景,得到各运动目标的估计区域;对每个运动目标建立一个独立的目标区域概率模型(OPM),随着检测帧数的迭代更新,以得到运动目标的准确区域。结果 多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题通过本文方法得到了很好地解决,Harris-Sift特征点提取、匹配时间仅为Sift特征的17%。在室外复杂环境下,本文方法的平均准确率比传统背景补偿方法高出14%,本文方法能从移动背景中准确分离出运动目标。结论 实验结果表明,该算法能满足实时要求,能够准确分离出运动目标区域和背景区域,且对相机运动、旋转,场景亮度变化等影响因素具有较强的鲁棒性。     

分裂合并运动分割的多运动视觉里程计方法

目的 视觉里程计（visual odometry,VO）仅需要普通相机即可实现精度可观的自主定位,已经成为计算机视觉和机器人领域的研究热点,但是当前研究及应用大多基于场景为静态的假设,即场景中只有相机运动这一个运动模型,无法处理多个运动模型,因此本文提出一种基于分裂合并运动分割的多运动视觉里程计方法,获得场景中除相机运动外多个运动目标的运动状态。方法 基于传统的视觉里程计框架,引入多模型拟合的方法分割出动态场景中的多个运动模型,采用RANSAC（random sample consensus）方法估计出多个运动模型的运动参数实例;接着将相机运动信息以及各个运动目标的运动信息转换到统一的坐标系中,获得相机的视觉里程计结果,以及场景中各个运动目标对应各个时刻的位姿信息;最后采用局部窗口光束法平差直接对相机的姿态以及计算出来的相机相对于各个运动目标的姿态进行校正,利用相机运动模型的内点和各个时刻获得的相机相对于运动目标的运动参数,对多个运动模型的轨迹进行优化。结果 本文所构建的连续帧运动分割方法能够达到较好的分割结果,具有较好的鲁棒性,连续帧的分割精度均能达到近100%,充分保证后续估计各个运动模型参数的准确性。本文方法不仅能够有效估计出相机的位姿,还能估计出场景中存在的显著移动目标的位姿,在各个分段路径中相机自定位与移动目标的定位结果位置平均误差均小于6%。结论 本文方法能够同时分割出动态场景中的相机自身运动模型和不同运动的动态物体运动模型,进而同时估计出相机和各个动态物体的绝对运动轨迹,构建出多运动视觉里程计过程。     

基于SAD与UKF-MeanShift的主动目标跟踪

针对复杂场景下动态目标难以准确分割以及目标难以准确定位的问题,提出将绝对差值和(SAD)方法、无迹卡尔曼滤波(UKF)和Mean shift算法相结合的混合自主跟踪动态目标的方法。首先,采用SAD方法获相邻两帧的视差信息,利用视差实现动态目标的检测,并依此建立目标的核直方图描述模型和状态空间模型,然后UKF算法对状态空间进行滤波估计,最后采用Mean shift 算法精确定位目标。实验结果表明该方法不仅能有效检测场景的动态目标,同时还能获得目标的运动信息。文中所提出的基于UKF-Mean shift的跟踪策略与相关算法相比,体现出较好的跟踪效果与时间性能。     

Intelligent visual surveillance — A survey

Detection, tracking, and understanding of moving objects of interest in dynamic scenes have been active research areas in computer vision over the past decades. Intelligent visual surveillance (IVS) refers to an automated visual monitoring process that involves analysis and interpretation of object behaviors, as well as object detection and tracking, to understand the visual events of the scene. Main tasks of IVS include scene interpretation and wide area surveillance control. Scene interpretation aims at detecting and tracking moving objects in an image sequence and understanding their behaviors. In wide area surveillance control task, multiple cameras or agents are controlled in a cooperative manner to monitor tagged objects in motion. This paper reviews recent advances and future research directions of these tasks. This article consists of two parts: The first part surveys image enhancement, moving object detection and tracking, and motion behavior understanding. The second part reviews wide-area surveillance techniques based on the fusion of multiple visual sensors, camera calibration and cooperative camera systems.     

Optimal feature points for tracking multiple moving objects in active camera model

Object tracking is an important task in computer vision that is essential for higher level vision applications such as surveillance systems, human-computer interaction, industrial control, smart compression of video, and robotics. Tracking, however, cannot be easily accomplished due to challenges such as real-time processing, occlusions, changes in intensity, abrupt motions, variety of objects, and mobile platforms. In this paper, we propose a new method to estimate and eliminate the camera motion in mobile platforms, and accordingly, we propose a set of optimal feature points for accurate tracking. Experimental results on different videos show that the proposed method estimates camera motion very well and eliminate its effect on tracking moving objects. And the use of optimal feature points results in a promising tracking. The proposed method in terms of accuracy and processing time has desirable results compared to the state-of-the-art methods.     

摄像机运动下基于背景匹配的运动目标检测

提出一种摄像机运动下的运动目标检测技术。首先获取相邻两帧图像上的对应特征点的坐标后应用到仿射变换模型得到全局运动参量来描述背景图像的运动变化。然后根据估计出的全局运动参量通过双线性内插法完成背景匹配。最后采用背景匹配后的两帧图像差进行目标检测。实验证明本算法在提高处理的精确度和时效性的同时还可以处理摄像机发生较大幅度旋转运动情况下的目标检测。     

Rigid body segmentation and shape description from dense opticalflow under weak perspective

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     

融合HOG与ARMA模型的粒子滤波跟踪

传统的粒子滤波算法在跟踪目标受到相似背景干扰和遮挡或跟踪目标高速运动时,容易造成跟踪误差增大或跟踪失效的影响。针对室外运动目标跟踪的复杂性,提出一种对于干扰适应性较强的融合梯度方向直方图与自回归移动平均(ARMA)模型的粒子滤波跟踪方法。建立ARMA运动模型,用前两帧目标的位姿状态预测目标下一帧的状态,解决目标跟踪的角度变化与部分遮挡问题。实验结果表明,该模型能克服光照突变引发目标色彩突变的问题。     

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.     

基于大数据分析的移动对象轨迹预测方法

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。     

Multi-object detection and tracking by stereo vision

This paper presents a new stereo vision-based model for multi-object detection and tracking in surveillance systems. Unlike most existing monocular camera-based systems, a stereo vision system is constructed in our model to overcome the problems of illumination variation, shadow interference, and object occlusion. In each frame, a sparse set of feature points are identified in the camera coordinate system, and then projected to the 2D ground plane. A kernel-based clustering algorithm is proposed to group the projected points according to their height values and locations on the plane. By producing clusters, the number, position, and orientation of objects in the surveillance scene can be determined for online multi-object detection and tracking. Experiments on both indoor and outdoor applications with complex scenes show the advantages of the proposed system.     

视频序列的全景图拼接技术

提出了一种对视频序列进行全景图拼接的方法。主要讨论了有大面积的非刚性运动物体出现的序列，不过此方法也同样适用于无运动物体的纯背景序列。为计算各帧间的投影关系，用仿射模型来描述摄像机运动，并用特征点匹配的方法计算出模型中各参数的值。由于用相关法计算的匹配结果准确率比较低，所以用RANSAC（Random Sampling Consensus）对匹配结果进行了筛选，可以准确求出摄像机运动参数。利用运动参数进行投影，然后用多帧相减并求交集，估计出每帧图像中运动物体存在的区域，最后计算得到了全景图。该方法的结果与前人得到的结果进行了比较，证明用此方法能获得质量较高的全景图。     

一种实时的主动跟踪方法

针对单视角像机的视野局限性以及连续跟踪特定目标时运动目标实时检测的困难，提出了一种离散化活动像机运动的方法。先离散化像机运动；建立像机预置位表和背景索引表；然后，利用目标位置信息和运动信息判断像机转动情况，并结合对应的控制原理，实现了像机的调度；最后，利用通过计算机视觉方法确定了特定目标在不同离散空间的对应关系，实现了对特定目标大范围的主动跟踪和运动目标的实时检测。实验结果表明，对于复杂场景中特定运动目标大范围的主动跟踪具有较好的鲁棒性和实时性，并且可以实时提取场景中的运动区域。     

Contour tracking with automatic motion model switching

In this paper we present an efficient contour-tracking algorithm which can track 2D silhouette of objects in extended image sequences. We demonstrate the ability of the tracker by tracking highly deformable contours (such as walking people) captured by a static camera. We represent contours (silhouette) of moving objects by using a cubic B-spline. The tracking algorithm is based on tracking a lower dimensional shape space (as opposed to tracking in spline space). Tracking the lower dimensional space has proved to be fast and efficient. The tracker is also coupled with an automatic motion-model switching algorithm, which makes the tracker robust and reliable when the object of interest is moving with multiple motion. The model-based tracking technique provided is capable of tracking rigid and non-rigid object contours with good tracking accuracy.     

Fast and accurate global motion compensation

Video understanding has attracted significant research attention in recent years, motivated by interest in video surveillance, rich media retrieval and vision-based gesture interfaces. Typical methods focus on analyzing both the appearance and motion of objects in video. However, the apparent motion induced by a moving camera can dominate the observed motion, requiring sophisticated methods for compensating for camera motion without a priori knowledge of scene characteristics. This paper introduces two new methods for global motion compensation that are both significantly faster and more accurate than state of the art approaches. The first employs RANSAC to robustly estimate global scene motion even when the scene contains significant object motion. Unlike typical RANSAC-based motion estimation work, we apply RANSAC not to the motion of tracked features but rather to a number of segments of image projections. The key insight of the second method involves reliably classifying salient points into foreground and background, based upon the entropy of a motion inconsistency measure. Extensive experiments on established datasets demonstrate that the second approach is able to remove camera-based observed motion almost completely while still preserving foreground motion.     

增强现实中的视频对象跟踪算法

根据增强现实在视频对象跟踪中的应用需求,提出一种综合利用尺度不变特征变换(SIFT)算子、K聚类算法和轮廓检测的视频对象跟踪算法。该算法利用简易SIFT获得输入图像的特征点,通过K聚类算法获得可能的对象聚类,并采用改进的轮廓处理方法得到对象边界,移除孤立点,确定对象特征点,在对象特征点中获取增强现实应用中需要的注册点。在关键帧匹配中,只要使用对象特征点进行对象匹配。实验结果表明,该算法具有运行速度快、匹配正确率高的特点,能满足增强现实视频应用的注册需求。     

基于视觉的无人飞艇地面目标检测

针对无人飞艇地面目标检测中细节信息缺失的问题,提出一种静态目标和运动目标的检测方法。利用Lucas-Kanade方法跟踪目标区域内特征点,从而实现静态目标的连续检测。通过图像特征点的跟踪估计相邻帧图像间的全局运动,进而对图像进行运动补偿,利用补偿后的帧差图实现运动目标的检测。采用上海交通大学“致远一号”无人飞艇采集的实际视频数据进行实验与分析,结果验证了该方法的有效性。     

ASSET-2: Real-Time Motion Segmentation and Object Tracking

This paper describes how image sequences taken by a moving video camera may be processed to detect and track moving objects against a moving background in real-time. The motion segmentation and shape tracking system is known as a scene segmenter establishing tracking, version 2 (ASSET-2). Motion is found by tracking image features, and segmentation is based on first-order (i.e. six-parameter) flow fields. Shape tracking is performed using two-dimensional radial map representations. The system runs in real-time, and is accurate and reliable. It requires no camera calibration and no knowledge of the camera's motion.     

Detecting independent motion: the statistics of temporal continuity

We consider a problem central in aerial visual surveillance applications; detection and tracking of small, independently moving objects in long and noisy video sequences. We directly use spatiotemporal image intensity gradient measurements to compute an exact model of background motion. This allows the creation of accurate mosaics over many frames, and the definition of a constraint violation function which acts as an indicator of independent motion. A novel temporal integration method maintains confidence measures over long subsequences without computing the optic flow, requiring object models, or using a Kalman filter. The mosaic acts as a stable feature frame, allowing precise localization of the independently moving objects. We present a statistical analysis of the effects of image noise on the constraint violation measure and find a good match between the predicted probability distribution function and the measured sample frequencies in a test sequence