Fragmentation handling for visual tracking

Object detection and tracking using background subtraction suffers from the fragmentation problem which means one object fragments into several blobs because of being similar with the reference image in color. In this paper, we build a visual tracking framework using background subtraction for object detection, and we address the association difficulty of blobs with objects caused by the fragmentation problem by two steps. We firstly cluster the blobs according to the boundary distances of them estimated by an approximating method proposed in this paper. Blobs clustered into the same blob-set are considered from the same object. Secondly, we consider blob-sets possibly from the same object if they exhibit coherent motion, since blobs of the same object may be clustered into different blob-sets if the object fragments severely. A background-matching method is proposed to determine whether two blob-sets exhibiting coherent motion are truly from the same object or from different objects. We test the proposed methods on several real-world video sequences. Quantitative and qualitative experimental results show that the proposed methods handle the problems caused by fragmentation effectively.     

Efficient multiple moving object detection and tracking using combined background subtraction and clustering

Object detection and tracking is a fundamental, challenging task in computer vision because of the difficulties in tracking. Continuous deformation of objects during movement and background clutter leads to poor tracking. In this paper, a method of multiple moving object detection and tracking by combining background subtraction and K-means clustering is proposed. The proposed method can handle objects occlusion, shadows and camera jitter. Background subtraction filters irrelevant information, and K-means clustering is employed to select the moving object from the remaining information, and it is capable of handling merging and splitting of moving objects using spatial information. Experimental results show that the proposed method is robust when compared to other techniques.     

Vehicle Tracking and Speed Measurement system (VTSM) based on novel feature descriptor: Diagonal Hexadecimal Pattern (DHP)

Object detection and tracking is an important and active research area in computer vision community. The proposed Vehicle Tracking and Speed Measurement (VTSM) system can find out speed parameters of the vehicles. Speed parameters are used to take judgment on accidents at a low cost. The main objective of this paper is to develop an algorithm that can detect foreground, track specified object and calculate speed parameter of the object. Identifying stationary background from moving objects in a video is a critical task. To achieve superior foreground detection quality across unconstrained scenarios, a novel dynamic background subtraction and object tracking algorithm using a novel Diagonal Hexadecimal Pattern (DHP) is proposed. Metric F-score and MOTA are used to measure the performance of the proposed system. From the results, it is observed that the proposed system gives good results for the background subtraction and tracking.     

视频序列中运动目标的检测与跟踪方法

提出了一种视频序列中运动目标的检测与跟踪算法,该算法采用基于码本背景建模的减背景法与差分法相结合的算法,实现对运动目标的快速精确的检测与提取,也能够在存在前景运动的过程中提取背景,使用卡尔曼滤波对运动目标在下一帧中最可能出现的位置进行估计,在此基础上利用Camshift跟踪算法进行较小范围的搜索和目标匹配,减少了运算量、节约了搜索和匹配的时间、提高了跟踪的速度。实验证明该方法具有一定的实用性。     

Intelligent Mobile Video Surveillance System with Multilevel Distillation

This paper proposes a mobile video surveillance system consisting of intelligent video analysis and mobile communication networking. This multilevel distillation approach helps mobile users monitor tremendous surveillance videos on demand through video streaming over mobile communication networks. The intelligent video analysis includes moving object detection/tracking and key frame selection which can browse useful video clips. The communication networking services, comprising video transcoding, multimedia messaging, and mobile video streaming, transmit surveillance information into mobile appliances. Moving object detection is achieved by background subtraction and particle filter tracking. Key frame selection, which aims to deliver an alarm to a mobile client using multimedia messaging service accompanied with an extracted clear frame, is reached by devising a weighted importance criterion considering object clarity and face appearance. Besides, a spatial-domain cascaded transcoder is developed to convert the filtered image sequence of detected objects into the mobile video streaming format. Experimental results show that the system can successfully detect all events of moving objects for a complex surveillance scene, choose very appropriate key frames for users, and transcode the images with a high power signal-to-noise ratio (PSNR).     

基于HSV空间阴影去除方法研究与应用

在智能视频监控系统中,运动阴影如果被误判为运动目标,将会影响到场景中运动目标的准确提取、跟踪和预测。针对这一问题,设计了一种基于HSV颜色空间的阴影去除方法。方法首先将背景差法和三帧差分法相结合,用于提取运动目标,再将提取的含有阴影的运动目标区域映射到其HSV色彩空间,通过与背景和相邻帧的亮度、饱和度比较,实现对阴影区域的检测和去除,处理过程中无需提前确定特征判别参数。将所设计的方法在标准高速公路视频数据库中进行测试并应用于实时的视频监控系统,验证结果表明该方法能更加有效的消除阴影,从而准确的检测出运动目标,同时方法对光线变化具有一定的鲁棒性。     

基于多个非刚体目标跟踪的视频对象平面生成算法

提出了一种提取运动对象的新的视频序列分割算法。算法的核心是一个对象跟踪器,它利用一种基于对象行为的跟踪算法对多个非刚体目标有效地进行对象跟踪,并与后续帧进行匹配,然后采用一种基于运动相连成分的模型刷新方法对模型的每一帧进行刷新,初始的模型自动产生,再利用滤波技术滤除静止背景,最后,利用边界图像模型从序列中提取出视频对象平面（VOP）。     

结合直方图反投影的多特征运动阴影检测算法

针对视频监控中运动阴影影响目标检测跟踪准确性的问题,提出一种结合直方图反投影的多特征运动阴影检测算法。首先利用背景减除法,得到前景区域并进行初步筛选;然后在背景区域建立亮度、颜色、梯度特征的联合直方图,以反投影的方式投影到前景区域得到运动阴影概率图;最后结合空间一致性和滞后阈值,对概率图进行分割得到运动阴影区域。与典型算法进行对比的实验结果表明,本文算法能够有效区分阴影与目标,适用于实时的运动目标检测与跟踪。     

视频监控运动目标检测算法研究

视频的运动目标跟踪有很多干扰因素,如目标改变、复杂场景、目标变形等。为了解决这些问题,通过对几个常用的运动目标检测方法进行比较分析,提出了一个通过帧间差分和背景消除进行运动目标检测和识别的算法。通过进行模拟实验,结果显示了该算法具有较高的效率。     

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。     

运动检测算法的研究和仿真实现

帧间差法和背景差法都是重要的运动检测方法,其核心问题在于如何得到准确的运动对象.针对该问题,本文提出一种结合帧间差和背景差的自动分割算法.该算法通过累积的帧差信息构建出可靠的背景,再将背景与当前帧比较,进而提取出视频运动对象.本文运用了最大类间方差法OTSU(又名"大津法")来获得自适应阈值,能更准确地对背景差图像进行阈值化分割,克服了传统固定阈值容易失效的问题.还采用了形态滤波的方法,对二值图像进行去噪,填充空洞.     

Automatic Segmentation of Moving Objects in Video Sequences for Indoor and Outdoor Applications

1　IntroductionAutomaticsegmentationofmovingobjectsfromvideosequencesisadifficultandchallengingproblemincomputervisionsystems.Ithasmanyapplicationssuchasvideosurveillance,trafficmonitoring ,peopletrackingandvideocommunication[1～4] .Italsoplaysanimportantroleinsupportingcontent basedimagecoding,especiallyaftertheemergenceofthevideocodingstandardMPEG 4[5～ 1 4 ] .Therearealotofresearchworksonmovingob jectssegmentationandextraction .Thesealgorithmscanberoughlyclassifiedintotwocategories:inter …     

基于背景差分的一种运动目标检测方法

针对运动目标检测中,传统背景差分法在运动目标和背景颜色相近时不易检测的缺点,提出了一种检测完整运动目标的方法。该方法对YUV彩色空间下的3个通道分别选取独立的阈值进行初次检测,最大化地利用了视频中图像的色彩信息。在包含初次检测所获运动目标的最小矩形区域内进行二次检测,有效地提高了检测精度。实验证明,相比于常规方法,该方法的检测结果更加清晰完整。     

Moving object detection and tracking from video captured by moving camera

This paper presents an effective method for the detection and tracking of multiple moving objects from a video sequence captured by a moving camera without additional sensors. Moving object detection is relatively difficult for video captured by a moving camera, since camera motion and object motion are mixed. In the proposed method, the feature points in the frames are found and then classified as belonging to foreground or background features. Next, moving object regions are obtained using an integration scheme based on foreground feature points and foreground regions, which are obtained using an image difference scheme. Then, a compensation scheme based on the motion history of the continuous motion contours obtained from three consecutive frames is applied to increase the regions of moving objects. Moving objects are detected using a refinement scheme and a minimum bounding box. Finally, moving object tracking is achieved using a Kalman filter based on the center of gravity of a moving object region in the minimum bounding box. Experimental results show that the proposed method has good performance.     

Segmentation and tracking of moving objects for content-based videocoding

To enable content-based functionalities in video coding, a decomposition of the scene into physical objects is required. Such objects are normally not characterised by homogeneous colour, intensity, or optical flow. Therefore, conventional techniques based on these low-level features cannot perform the desired segmentation. The authors address segmentation and tracking of moving objects and present a new video object plane (VOP) segmentation algorithm that extracts semantically meaningful objects. A morphological motion filter detects physical objects by identifying areas that are moving differently from the background. A new filter criterion is introduced that measures the deviation of the estimated local motion from the synthesised global motion. A two-dimensional binary model is derived for the object of interest and tracked throughout the sequence by a Hausdorff object tracker. To accommodate for rotations and changes in shape, the model is updated every frame by a two-stage method that accounts for rigid and non-rigid moving parts of the object. The binary model then guides the actual VOP extraction, whereby a novel boundary post-processor ensures high boundary accuracy. Experimental results demonstrate the performance of the proposed algorithm     

Video object segmentation in rainy situations based on difference scheme with object structure and color analysis

A scheme based on a difference scheme using object structures and color analysis is proposed for video object segmentation in rainy situations. Since shadows and color reflections on the wet ground pose problems for conventional video object segmentation, the proposed method combines the background construction-based video object segmentation and the foreground extraction-based video object segmentation where pixels in both the foreground and background from a video sequence are separated using histogram-based change detection from which the background can be constructed and detection of the initial moving object masks based on a frame difference mask and a background subtraction mask can be further used to obtain coarse object regions. Shadow regions and color-reflection regions on the wet ground are removed from the initial moving object masks via a diamond window mask and color analysis of the moving object. Finally, the boundary of the moving object is refined using connected component labeling and morphological operations. Experimental results show that the proposed method performs well for video object segmentation in rainy situations.     

基于高斯金字塔与差分法的多目标检测和跟踪算法

针对复杂背景下的多目标检测和跟踪问题,提出了将背景差分目标检测算法与高斯金字塔图像重采样相结合的运动目标检测算法．该算法采用高斯金字塔法对图像进行重采样,建立背景模型,使用背景差分法获得前景区域,并对前景区域进行阴影检测、去除,从而检测出完整目标．融入了高斯模型关于背景更新的算法,克服了由于背景突然改变而造成的误检测．在目标阈值的确定过程中,采用动态阈值确定法,以提高目标检测的正确性．同时将目标的颜色特征和运动矢量引入到多目标跟踪算法中,提高目标跟踪的准确性．实验结果表明,该算法对于场景中存在目标频繁出现、消失、交叉运动和遮挡等情形均有较好的检测与跟踪效果．     

一种有效的图像阴影自动去除算法

目标跟踪与检测研究中,在检测运动前景时也会检测到运动目标投射的阴影。阴影使得运动目标发生几何变形,可能造成运动目标粘连,甚至造成检测不到目标。阴影去除后才能较真实的得到运动目标重心。本文研究一种利用图像YCbCr颜色信息去除阴影的方法。首先利用背景减的方法得到带影子的目标区域,其次进行YCbCr空间的背景减,由于影子和目标物体在YCbCr空间背景减信息有较大差别,因此可以通过阈值判断得到去影之后的精确目标区域,目标物体识别的精确性和鲁棒性将会得到提高。实验结果表明,该方法在去除阴影的同时又较好地保留了前景目标的信息,是一种有效的阴影去除方法。      

基于OpenCV的交通视频运动目标检测与跟踪

智能交通系统（ITS）是目前世界交通运输领域正在研究和广泛关注的课题。OpenCV是一种用于数字图像处理和计算机视觉的函数库,由Intel公司开发。本文在目标检测方面,对采集到的交通视频进行灰度化、中值滤波、背景建模、二值化,背景差分等处理,可以较准确地检测出运动目标。在目标跟踪方面,提出了CamShift算法和Kalman滤波器相结合的方法,实现视频车辆的精确跟踪。最后,利用OpenCV的运动物体跟踪的数据结构、函数库,建立了一个视频车辆分析系统。用于道路上车辆的检测与跟踪,并具有良好的鲁棒性。