Probabilistic and sequential computation of optical flow usingtemporal coherence

In the computation of dense optical flow fields, spatial coherence constraints are commonly used to regularize otherwise ill-posed problem formulations, providing spatial integration of data. We present a temporal, multiframe extension of the dense optical flow estimation formulation proposed by Horn and Schunck (1981) in which we use a temporal coherence constraint to yield the optimal fusing of data from multiple frames of measurements. Conceptually, standard Kalman filtering algorithms are applicable to the resulting multiframe optical flow estimation problem, providing a solution that is sequential and recursive in time. Experiments are presented to demonstrate that the resulting multiframe estimates are more robust to noise than those provided by the original, single-frame formulation. In addition, we demonstrate cases where the aperture problem of motion vision cannot be resolved satisfactorily without the temporal integration of data enabled by the proposed formulation. Practically, the large matrix dimensions involved in the problem prohibit exact implementation of the optimal Kalman filter. To overcome this limitation, we present a computationally efficient, yet near-optimal approximation of the exact filtering algorithm. This approximation has a precise interpretation as the sequential estimation of a reduced-order spatial model for the optical flow estimation error process at each time step and arises from an estimation-theoretic treatment of the filtering problem. Experiments also demonstrate the efficacy of this near-optimal filter.     

基于嵌入式车流量实时检测算法研究与实现

针对道路现场实时车流量检测问题,提出了一种改进的帧间差分法的运动车辆检测算法,并将该检测算法成功移植到了嵌入式系统上。将帧间差分法与采用长度、宽度、面积筛选轮廓及用质心距离的车辆跟踪算法结合,实现运动车辆的检测;将U-Boot引导程序、Linux内核、Yaffs2文件系统和检测算法移植到S3C6410上,通过摄像头实时采集交通视频,检测结果由触摸屏显示。复杂交通场景的实时测试结果表明,本系统的检测时间为0.298秒/帧,准确率超过88%,基本能够实现在道路现场的车流量实时检测。     

Variational optical flow computation in real time.

This paper investigates the usefulness of bidirectional multigrid methods for variational optical flow computations. Although these numerical schemes are among the fastest methods for solving equation systems, they are rarely applied in the field of computer vision. We demonstrate how to employ those numerical methods for the treatment of variational optical flow formulations and show that the efficiency of this approach even allows for real-time performance on standard PCs. As a representative for variational optic flow methods, we consider the recently introduced combined local-global method. It can be considered as a noise-robust generalization of the Horn and Schunck technique. We present a decoupled, as well as a coupled, version of the classical Gauss-Seidel solver, and we develop several multgrid implementations based on a discretization coarse grid approximation. In contrast, with standard bidirectional multigrid algorithms, we take advantage of intergrid transfer operators that allow for nondyadic grid hierarchies. As a consequence, no restrictions concerning the image size or the number of traversed levels have to be imposed. In the experimental section, we juxtapose the developed multigrid schemes and demonstrate their superior performance when compared to unidirectional multgrid methods and nonhierachical solvers. For the well-known 316 x 252 Yosemite sequence, we succeeded in computing the complete set of dense flow fields in three quarters of a second on a 3.06-GHz Pentium4 PC. This corresponds to a frame rate of 18 flow fields per second which outperforms the widely-used Gauss-Seidel method by almost three orders of magnitude.     

动态背景下的光流场计算方法

通过计算光流场来检测场景中的运动目标是计算机视觉中非常重要的研究课题,而光流场计算的精度直接关系到目标检测的准确性。针对实际拍摄的视频中由于背景存在运动而导致光流场中运动目标不突出的情况,提出了一种基于分块积分投影配准算法的光流场计算方法。首先利用提出的分块积分投影配准算法得到图像背景的运动参数,然后对背景进行运动补偿,再利用L-K算法求取运动补偿后图像中有效区域的光流场。通过真实视频对算法进行验证,并将结果与经典的L-K算法结果进行了对比。对比结果显示:本文所提算法计算得到的光流场中运动目标更加突出,算法效果较好。     

Motion detection using block based bi-directional optical flow method

Detecting moving objects from video frame sequences has a lot of useful applications in computer vision. This proposed method of moving object detection first estimates the bi-directional optical flow fields between (i) the current frame and the previous frame and between (ii) the current frame and the next frame. The bi-directional optical flow field is then subjected to normalization and enhancement. Each normalized and enhanced optical flow field is then divided into non-overlapping blocks. The moving objects are finally detected in the form of binary blobs by examining the histogram based thresholded values of such optical flow field of each block as well as the optical flow field of the candidate flow value. Our technique has been conceptualized, implemented and tested on real video data sets with complex background environment. The experimental results and quantitative evaluation establish that our technique achieves effective and efficient results than other existing methods.     

Robust real-time-constrained estimation of respiratory motion for interventional MRI on mobile organs

Real-time magnetic resonance imaging is a promising tool for image-guided interventions. For applications such as thermotherapy on moving organs, a precise image-based compensation of motion is required in real time to allow quantitative analysis, retrocontrol of the interventional device, or determination of the therapy endpoint. Reduced field-of-view imaging represents a promising way to improve spatial and/or temporal resolution. However, it introduces new challenges for target motion estimation, since structures near the target may appear transiently due to the respiratory motion and the limited spatial coverage. In this paper, a new image-based motion estimation method is proposed combining a global motion estimation with a novel optical flow approach extending the initial Horn and Schunck (H&S) method by an additional regularization term. This term integrates the displacement of physiological landmarks into the variational formulation of the optical flow problem. This allowed for a better control of the optical flow in presence of transient structures. The method was compared to the same registration pipeline employing the H&S approach on a synthetic dataset and in vivo image sequences. Compared to the H&S approach, a significant improvement (p<0.05) of the Dice's similarity criterion computed between the reference and the registered organ positions was achieved.     

On convergence of the Horn and Schunck optical-flow estimation method

The purpose of this study is to prove convergence results for the Horn and Schunck optical-flow estimation method. Horn and Schunck stated optical-flow estimation as the minimization of a functional. When discretized, the corresponding Euler-Lagrange equations form a linear system of equations We write explicitly this system and order the equations in such a way that its matrix is symmetric positive definite. This property implies the convergence Gauss-Seidel iterative resolution method, but does not afford a conclusion on the convergence of the Jacobi method. However, we prove directly that this method also converges. We also show that the matrix of the linear system is block tridiagonal. The blockwise iterations corresponding to this block tridiagonal structure converge for both the Jacobi and the Gauss-Seidel methods, and the Gauss-Seidel method is faster than the (sequential) Jacobi method.     

一种基于OpenCL的Lukas-Kanade光流并行加速算法

LK(Lukas-Kanade)光流法在运动目标检测和跟踪领域具有广泛应用,但其计算复杂、速度慢,难以适应异构硬件平台。为实现LK光流法在不同平台上的高效运行,设计了一种基于开放式计算语言（OpenCL）的LK光流法并行算法。该算法通过将二维图像上像素点上的稠密计算映射到多线程上实现数据并行,并基于OpenCL平台的共享内存等优化方法减小了主机内存与设备内存数据传输。实验测试表明,该算法相比于多核CPU下的基础OpenCV函数库中的LK算法获得了最高31倍的加速比,同时在速度上与统一计算设备体系结构（CUDA）加速的LK光流法相近。此外,还在多种不同设备下验证了加速算法的平台通用性。     

基于OpenCV的运动目标跟踪系统研究

本文分析比较了传统运动目标检测的3种主要方法：背景图像差分法、时态差分法和光流法,在此基础上给出了一种背景图像预测算法,大大减少了因为背景变化而产生的目标检测误差。本文基于OpenCV设计出改进的运动目标检测与跟踪算法,实现了运动目标的跟踪,并在VC＋＋编译环境下,利用USB摄像头作为视频采集器,通过观察实验结果可以看出,本文的运动目标检测算法能够正确地检测出视频图像中的运动目标,而且在检测性能上优于普通的自适应背景差分法。     

一种复杂背景下运动小目标的检测方法

本文将小目标的帧间信息和光流法紧密联系起来,把小目标的检测分为小目标的预处理、帧间差分和使用金字塔迭代Lucas-Kanade的光流法确定目标三个步骤进行．实验结果表明,该方法能够有效地检测运动小目标．     

基于帧间差分和金字塔LK光流法的运动目标检测

针对用于运动目标检测的光流算法存在处理复杂、计算量大等问题,提出一种帧间差分算法和金字塔LK光流法相结合的运动目标检测方案.该方法先对视频图像进行帧间差分处理,得到图像的运动区域,再对该运动区域进行金字塔LK光流计算,减少了计算区域,提高目标检测的速度.最后在搭建的视觉避障平台上使用LabVIEW语言进行算法程序验证,实验结果证明了算法的有效性.     

Optimal brightness functions for optical flow estimation ofdeformable motion

Estimation accuracy of Horn and Schunck's (1981) classical optical flow algorithm depends on many factors including the brightness pattern of the measured images. Since some applications can select brightness functions with which to "paint" the object, it is desirable to know what patterns will lead to the best motion estimates. The paper presents a method for determining this pattern a priori using mild assumptions about the velocity field and imaging process. The method is based on formulating Horn and Schunck's algorithm as a linear smoother and rigorously deriving an expression for the corresponding error covariance function. The authors then specify a scalar performance measure and develop an approach to select an optimal brightness function which minimizes this performance measure from within a parametrized class. Conditions for existence of an optimal brightness function are also given. The resulting optimal performance is demonstrated using simulations, and a discussion of these results and potential future research is given.     

采用光流估计的数字相机自动对焦算法

自动对焦技术对于数字相机至关重要，它是获取清晰图像的重要手段。针对复杂环境下多目标场景图像，提出了一种基于光流场估计的自动对焦算法。通过计算输入图像序列的光流场，对场景中的运动目标进行检测，根据目标运动属性准确判断出感兴趣目标。改进了Brenner清晰度评价方法，利用目标的二维边缘梯度信息建立评价函数，并且通过非线性增益提高评价函数的灵敏度，减小了噪声对评价值的影响。实验证明，该算法能够在主辅目标景深比达50倍的情况下分辨出感兴趣主目标，并在方差为0.02的随机噪声干扰下能有效地评价图像的清晰度；此算法将Brenner等评价函数的峰值稳定余量提高了1至4倍，对于不同图像具有良好的鲁棒性，易于硬件实现。     

基于单目视觉的车辆行驶方向检测及误差分析

智能车辆中一个重要的技术就是如何准确计算车辆前进方向与道路的夹角,目前的参考文献中基本上都是假设相机光轴与地面平行,而忽略了相机的安装误差,本文基于单目摄像机利用机器视觉方法与几何方法完成了对车辆行进方向与道路夹角的检测,给出了完整的技术实现方案,并重点分析了摄像机光轴方向与地面成一定角度情况下的校正方法,从而减少了摄像机安装误差对测量结果的影响,拓展了道路夹角检测技术的应用范围,使其有了更广的适用性,实际拍摄的场景图像也验证了本文算法的有效性。      

智能交通系统运动车辆的光流法检测

基于视频的车辆检测在智能交通系统中有着重要的实用价值.提出了一种在复杂背景中检测运动车辆的方法,针对传统光流法在阴影、边界和遮挡的地方灰度守恒和光流场平滑性假设不再成立这一问题,引入前向-后向光流方程,计算其Hessian矩阵,并把Hessian矩阵的条件数与Lucas-Kanade光流法中的加权阵相结合,有效地消除了局部邻域中不可靠的约束点,同时进一步提高了光流约束方程解的稳定性.实验结果表明：该方法检测情况稳定,检测准确率高,检测效果好.检测结果可作为智能交通系统中高层交通管理和车辆控制的基础.     

一种自适应的光流估计方法

本文通过对光流基本约束项和平滑约束项加以改进,提出一种自应用的光流估计算法。这种算不能够较精确进行光流运动估计,减轻Horn方法中全局平滑约束对运动边界的影响,提高估计的峰值信噪比。实验结果表明：这种算法是有效的。     

A frequency domain performance analysis of Horn and Schunck''soptical flow algorithm for deformable motion

A frequency domain performance analysis of Horn and Schunck's (1981) optical flow (HSOF) algorithm for estimation of deformable motion is presented. Noise sources in the algorithm are modeled using the discrete Fourier transform of the brightness pattern. This noise model along with the estimation error covariance function derived in previous work is used to derive an expression for the expected performance of the optical flow estimate that is valid for an arbitrary discrete brightness pattern. Simulation results are presented that demonstrate the validity of our methods and show that HSOF is more accurate that the optical flow estimate of Anandan for certain low-frequency patterns.     

Toward a deep CNN and RS-GOA framework for vehicle detection,traffic flow estimation,and optimal path selection from surveillance videos

Several autonomous traffic monitoring systems have been created as a result of the growing number of vehicles in urban areas. Traffic surveillance systems that use roadside cameras, in particular, are becoming widely used for traffic management. For an efficient traffic control and vehicle navigation system, accurate traffic flow information must be obtained based on the vehicles detected in surveillance videos. However, vehicles of various scales are difficult to spot in traffic surveillance videos due to the presence of barricades, other vehicles, and the impact of poor lighting. Also, adverse weather conditions like snow, fog, and heavy rain diminish the visual quality of the surveillance footage. This paper proposes multi-scale dense nested deep CNN (MSDN-DCNN) and regional search grasshopper optimization algorithm (RS-GOA) framework to accurately detect the vehicles, estimate the traffic flow, and find the optimal path with less travel time. First, the surveillance videos are pre-processed, which includes frame conversion, redundancy removal, and image enhancement. The pre-processed frames are given as input to the MSDN-DCNN for multi-scale vehicle detection. The detected results are used for vehicle counting and estimating the traffic flow. Finally, the optimal path is chosen based on the traffic flow information by using the RS-GOA algorithm. The performance of the proposed method is compared with the existing vehicle detection and path selection techniques. The results illustrate that the proposed Deep CNN-RS-GOA framework has improved performance with high detection accuracy (91.03%), high speed (53.9 fps), less running time (1,000 ms), less travel time, and faster convergence.     

Moving object segmentation and detection for monocular robot based on active contour model

A novel geodesic active contour model based on optical flow information is proposed to segment and detect the moving object for monocular robots. More specifically, an active contour is formulated using the level set method, which eliminates the need of re-initialisation. The developed scheme alleviates the effect of optical flow noise, increasing the robustness of the detection of moving objects. Experimental results show that this algorithm can successfully track a moving target, e.g. a human being.     

基于改进的Camshift运动目标跟踪算法的研究

针对基于颜色概率分布的连续自适应均值漂移算法(Camshift)跟踪算法在背景中出现相同颜色干扰时容易致使跟踪目标失败的问题,提出了一种改进的Camshift跟踪算法。首先对Camshift跟踪目标前进行目标检测,通过帧差法、光流法、背景差分法三种检测算法对比,采用背景差分法得到的运动目标区域矩形特征参数作为Camshift的初始化参数,取代一般Camshift算法利用颜色特征的跟踪。最后对改进的算法和一般Camshift进行仿真对比实验。实验结果表明,结合背景差分法和连续Camshift算法的运动目标跟踪在一定程度上满足了实时性与稳定性的要求。