A moving object segmentation algorithm for static camera via active contours and GMM

Moving object segmentation is one of the most challenging issues in computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Our algorithm integrates the GMM background model, shadow elimination term and curve evolution edge stopping term into energy function. It achieves more accurate segmentation than existing methods of the same type. Promising results on real images demonstrate the potential of the presented method. Supported by National Basic Research Program of China (Grant No. 2006CB303105), the Chinese Ministry of Education Innovation Team Fund Project (Grant No. IRT0707), the National Natural Science Foundation of China (Grant Nos. 60673109 and 60801053), Beijing Excellent Doctoral Thesis Program (Grant No. YB20081000401), Beijing Municipal Natural Science Foundation (Grant No. 4082025), and Doctoral Foundation of China (Grant No. 20070004037)     

Simultaneous active camera array focus plane estimation and occluded moving object imaging

Automatically focusing and seeing occluded moving object in cluttered and complex scene is a significant challenging task for many computer vision applications. In this paper, we present a novel synthetic aperture imaging approach to solve this problem. The unique characteristics of this work include the following: (1) To the best of our knowledge, this work is the first to simultaneously solve camera array auto focusing and occluded moving object imaging problem. (2) A unified framework is designed to achieve seamless interaction between the focusing and imaging modules. (3) In the focusing module, a local and global constraint-based optimization algorithm is presented to dynamically estimate the focus plane of the moving object. (4) In the imaging module, a novel visibility analysis based active synthetic aperture imaging approach is proposed to remove the occluder and significantly improve the quality of occluded object imaging. An active camera array system has been set up and evaluated in challenging indoor and outdoor scenes. Extensive experimental results with qualitative and quantitative analyses demonstrate the superiority of the proposed approach compared with state-of-the-art approaches.     

一种改进的运动目标精确检测算法

针对传统混合高斯背景模型在多变场景下因背景模型更新不及时而存在的误检、漏检等不足,提出一种改进算法.该算法首先通过在高斯分布匹配过程中结合帧间差分获取的帧间未变化区域与变化区域判断像素点的区域类别,然后根据不同的像素区域类别执行不同的背景更新策略,使背景的更新及时准确地反映背景的变化.实验结果表明,该改进混合高斯背景模型算法能有效地解决因目标和背景相互转化而出现的拖尾、影子以及运动目标空洞等问题.     

A fast DGPSO-motion saliency map based moving object detection

Multimedia Tools and Applications - The rapid development in the field of computer vision has encouraged researchers to develop vision systems for moving object detection in embedded surveillance...     

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。     

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.     

摄像机位姿的高精度快速求解

目的 针对对应点个数大于等于6的摄像机位姿估计问题,提出一种既适用于已标定也适用于未标定摄像机的时间复杂度为 的高精度快速算法。 方法 首先选取四个非共面虚拟控制点,并根据空间点和虚拟控制点的空间关系以及空间点的图像建立线性方程组,以此求解虚拟控制点的图像坐标及摄像机内参,再由POSIT算法根据虚拟控制点及其图像坐标求解旋转矩阵和平移向量。 结果 模拟数据实验和真实图像实验表明该算法时间复杂度和计算精度均优于现有的已标定摄像机位姿的高精度快速求解算法EPnP。 结论 该算法能够同时估计摄像机内外参数,而且比现有算法具有更好的速度和精度。     

Multistage particle windows for fast and accurate object detection

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.     

New trends on moving object detection in video images captured by a moving camera: A survey

This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods in the case of moving camera. Most methods in this field can be classified into four categories; modeling based background subtraction, trajectory classification, low rank and sparse matrix decomposition, and object tracking. We discuss in details each category and present the main methods which proposed improvements in the general concept of the techniques. We also present challenges and main concerns in this field as well as performance metrics and some benchmark databases available to evaluate the performance of different moving object detection algorithms.     

Robust moving object detection against fast illumination change

To solve the problem due to fast illumination change in a visual surveillance system, we propose a novel moving object detection algorithm for which we develop an illumination change model, a chromaticity difference model, and a brightness ratio model. When fast illumination change occurs, background pixels as well as moving object pixels are detected as foreground pixels. To separate detected foreground pixels into moving object pixels and false foreground pixels, we develop a chromaticity difference model and a brightness ratio model that estimates the intensity difference and intensity ratio of false foreground pixels, respectively. These models are based on the proposed illumination change model. Based on experimental results, the proposed method shows excellent performance under various illumination change conditions while operating in real-time.     

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。     

联合外形响应的深度目标追踪器

针对追踪器使用卷积网络提取出来的特征模板进行目标位置匹配时，易产生响应噪声的问题，本文提出一种联合外形响应和卷积响应的深度目标追踪方法。在当前帧中，由前一帧提供的目标信息先分别提取卷积特征和外形信息，然后获得相应的卷积位置响应和外形位置响应；最后利用外形位置响应对卷积位置响应进行修正，从而有效地抑制响应噪声。实验表明:这种方法具有较高的位置精度，能够提高目标跟踪的准确性。     

基于改进混合高斯模型的运动物体检测研究

针对传统高斯建模的初始化问题、参数值的计算依赖于先前所有帧和零散噪点较多等问题,提出了一种改进混合高斯模型的方法,即在初始化每个像素点时采用邻域特性和中值滤波相结合的方法,用来获取更接近实际的初始背景。同时对背景模型的更新提出了改进方法,在原有的背景排序基础上增加“定时清零”策略,使新加入的像素点能快速匹配。最后对特定区域的学习速率进行重新设定,再结合像素点的空间分布特性,达到消除零散噪点和部分空洞的目的。实验结果表明,与传统的混合高斯模型相比,本文算法能准确的检测出运动物体,并对阴影和噪音有一定的抑制作用。     

一种主动移动侦测布防方法*

提出一种基于颜色与形状特征的移动侦测区域主动布防方法。以实际监控场景中特殊区域的颜色特征为基础,初步提取出含有噪声的若干块感兴趣区域（ROI）,然后分别与采用傅里叶描述子描述的几何形状库进行边界匹配,从而实现监控场景中侦测区域的自动布防。实验结果表明,该方法具有高效率、低运算量及较强的抗干扰性、鲁棒性。     

A time series representation model for accurate and fast similarity detection

Similarity search and detection is a central problem in time series data processing and management. Most approaches to this problem have been developed around the notion of dynamic time warping, whereas several dimensionality reduction techniques have been proposed to improve the efficiency of similarity searches. Due to the continuous increasing of sources of time series data and the cruciality of real-world applications that use such data, we believe there is a challenging demand for supporting similarity detection in time series in a both accurate and fast way. Our proposal is to define a concise yet feature-rich representation of time series, on which the dynamic time warping can be applied for effective and efficient similarity detection of time series. We present the Derivative time series Segment Approximation (DSA) representation model, which originally features derivative estimation, segmentation and segment approximation to provide both high sensitivity in capturing the main trends of time series and data compression. We extensively compare DSA with state-of-the-art similarity methods and dimensionality reduction techniques in clustering and classification frameworks. Experimental evidence from effectiveness and efficiency tests on various datasets shows that DSA is well-suited to support both accurate and fast similarity detection.     

面向对象的全时域移动对象数据模型

在面向对象模型中加入对动态属性的支持,提出和建立了一种面向对象的支持全时域数据存储和查询的移动对象数据模型。探讨了GPS定位信息中精度、方位等信息对移动对象位置更新的影响,并给出了一种新的基于定位精度、速度和方位的动态阈值位置更新策略,解决了移动对象的全时域存储和查询方法。最后对面向对象的全时域移动对象数据模型进行了验证,实验表明该动态阈值位置更新策略在不影响移动对象运动轨迹精度的情况下,可以有效减少位置更新,节省数据传输流量,减少数据存储量。     

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。     

Detection of moving objects with a moving camera using non-panoramic background model

This paper presents a fast and reliable method for moving object detection with moving cameras (including pan–tilt–zoom and hand-held cameras). Instead of building large panoramic background model as conventional approaches, we construct a small-size background model, whose size is the same as input frame, to decrease computation time and memory storage without loss of detection performance. The small-size background model is built by the proposed single spatio-temporal distributed Gaussian model and this can solve false detection results arising from registration error and background adaptation problem in moving background. More than the proposed background model based on spatial and temporal information, several pre- and post-processing methods are adopted and organized systematically to enhance the detection performances. We evaluate the proposed method with several video sequences under difficult conditions, such as illumination change, large zoom variation, and fast camera movement, and present outperforming detection results of our algorithm with fast computation time.     

A fast algorithm for automatic segmentation and extraction of a single object by active surfaces

Segmentation is an important problem in various applications. There exist many effective models designed to locate all features and their boundaries in an image. However such global models are not suitable for automatically detecting a single object among many objects of an image, because nearby objects are often selected as well. Several recent works can provide selective segmentation capability but unfortunately when generalized to three dimensions, they are not yet effective or efficient. This paper presents a selective segmentation model which is inherently suited for efficient implementation. With the added solver by a fast nonlinear multigrid method for the inside domain of a zero level set function, the over methodology leads to an effective and efficient algorithm for 3D selective segmentation. Numerical experiments show that our model can produce efficient results in terms of segmentation quality and reliability for a large class of 3D images.     

Estimating the position of a moving object based on test disturbance of camera position

The problem of estimating the coordinates of a moving object based on visual data arises in numerous applications, starting from robotic and ending with the consumer market of portable devices. Traditional algorithms for solving this problem require either additional devices or significant constraints on the possible motion of the object. In this work, we present a new approach to tracking the object that lets us estimate its position under sufficiently general conditions. The method is based on randomizing the camera location independently of the object’s motion; since the test disturbance we choose is independent, it lets us construct a feasible iterative pseudogradient estimation algorithm.