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。     

一种改进的基于纹理和颜色的运动阴影检测

精确地消除活动阴影对运动目标的影响是智能视频监控的核心任务之一,针对当前运动阴影检测中采用的纹理信息过于粗糙、阈值选取需要人工干涉等问题,通过对NCC(归一化互相关)纹理算法进行改进,并结合亮度和归一化颜色特性,提出一种自适应的运动阴影检测方法。以混合高斯模型得到的前景像素为基础,通过阴影在亮度和归一化颜色的特性筛选出候选的阴影区域,结合改进的纹理算法进一步缩小阴影区域范围,最后利用空间后处理得到真实阴影。实验结果表明,该算法在有效降低噪声干扰的情况下能够较好区分局部纹理不明显的运动目标和阴影。     

基于运动跟踪匹配技术的视频对象提取

本文提出一种自动分割VOP的技术。其方法是：先对初始帧使用形态运动滤波技术提取出初始运动对象的二值轮廓模型,并在后继帧中使用豪斯道夫对象跟踪器跟踪运动以对象模型;而为了适应对象的形状变化,本文使用活动轮廓模型（snake）技术对运动心合匹配;最后根据一系列精确的二值轮廓引导提取运动对象序列。实验结果表明,我们的算法可有效地提取视频对象平面。     

Moving cast shadow detection using online sub-scene shadow modeling and object inner-edges analysis

In this paper, we propose an adaptive and accurate moving cast shadow detection method employing online sub-scene shadow modeling and object inner-edges analysis for applications of static-camera video surveillance. To describe shadow appearance more accurately, the proposed method builds adaptive online shadow models for sub-scenes with different conditions of irradiance and reflectance. The online shadow models are learned by utilizing Gaussian functions to fit the significant peaks of accumulating histograms, which are calculated from Hue, Saturation and Intensity (HSI) difference of moving objects between background and foreground. Additionally, object inner-edges analysis is adopted to reject camouflages, which are misclassified foreground regions that are highly similar to shadows. Finally, the main shadow regions are expanded to recycle the misclassified shadow pixels based on local color constancy. The proposed algorithm can adaptively handle the shadow appearance changes and camouflages without prior information about illuminations and scenarios. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.     

基于Snake活动轮廓模型的视频跟踪分割方法

基于Snake活动轮廓模型,采用时空融合的方式,根据短时间内相邻帧的运动趋势差异相似的前提,首先将视频序列分成若干个小段,每段有k帧视频,选取段内的前两帧为关键帧,通过运动检测的方式自动得到这两帧中运动对象的大致区域;然后进行帧内Snake演变,搜索精确轮廓;最后以关键帧间运动对象形心的运动矢量预测勾勒后续帧的初始轮廓,进行帧内Snake精确轮廓定位,从而实现所有帧的视频对象分割。相比于传统方法,本文方法克服了手动绘制初始轮廓的缺点,在空域对Snake贪婪方法进行了改进而且精确度高,速度快。实验表明,本文方法成功地实现了前后帧图像之间运动对象的对应匹配关系,并通过改进后的Snake贪婪方法得到了精确的分割结果。     

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.     

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods.     

基于帧差法的运动车辆阴影去除算法

在视频交通车辆目标检测中,阴影问题是影响其检测准确性的关键问题之一.为了解决这个问题,提出了一种结合单模高斯模型和帧差法的运动目标阴影去除方法.首先通过单模高斯模型背景建模获取前景包括阴影在内的目标图像,再结合帧差法去除阴影.实验结果证明,该方法得到的车辆目标比较完整,并较好地去除了运动车辆阴影.     

Shadow generation from stereo images

A novel approach to generating object shadows using two dimensional (2D) information is presented. Existing approaches to shadow generation are generally based on 3D geometry. The proposed method is based on deriving accurate shadows of a 3D object using only stereo images of the object taken from two different viewpoints. The approach can be extended easily to solve more general problems which involve multiple objects as well as multiple planar surfaces on which object shadows are to be cast     

An automatic segmentation method for moving objects based on the spatial-temporal information of video

The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find pre~：ise object boundaries of moving objects. To locate moving objects in image sequences, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.     

混合交通环境中的阴影检测算法

在城市交通流量视频检测系统中,目标阴影总是干扰对目标的正确检测和识别。在混合交通环境下,传统的阴影检测算法总是避免不了进行边缘检测、模板匹配等运算,不仅处理速度慢,而且对行人阴影的检测效果不好。本文提出一种基于颜色信息的阴影检测算法,该算法首先在图像中检测出运动区域,然后在运动区域内计算目标R、G、B颜色分量的灰度距离和色彩距离;最后根据这两个距离量检测出区域中的阴影。实验表明,该算法能够正确检测出车辆和行人的阴影,还能在雨天对目标的路面倒影进行检测,而且计算速度较快。     

Robust real-time segmentation of images and videos using a smooth-spline snake-based algorithm.

This paper deals with fast image and video segmentation using active contours. Region-based active contours using level sets are powerful techniques for video segmentation, but they suffer from large computational cost. A parametric active contour method based on B-Spline interpolation has been proposed in to highly reduce the computational cost, but this method is sensitive to noise. Here, we choose to relax the rigid interpolation constraint in order to robustify our method in the presence of noise: by using smoothing splines, we trade a tunable amount of interpolation error for a smoother spline curve. We show by experiments on natural sequences that this new flexibility yields segmentation results of higher quality at no additional computational cost. Hence, real-time processing for moving objects segmentation is preserved.     

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.     

室内夜间微弱光源下运动目标检测和阴影去除

提出一种新的室内夜间微弱光源照明情况下的运动目标检测方法。首先进行背景建模, 获取稳固的背景图像, 之后对背景和当前帧图像进行图像增强处理, 提高其清晰度; 采用相对背景减法检测前景运动目标, 并对差分图像进行去噪和修补; 利用前景目标区域、阴影区域和背景区域像素亮度值存在差异的特点, 检测和去除背景差分图像中可能存在的阴影, 获得准确的运动目标。在室内夜间环境下采集视频进行试验, 结果验证了所提方法的有效性。     

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     

Robust moving shadow detection with hierarchical mixture of MLP experts

Detection and elimination of the shadows of moving objects in video sequences have been one of the major challenges in tracking applications. Since moving shadows cannot be removed from foreground by motion-based background subtraction methods, they lead to confusion and error in moving object tracking. In this paper, a novel classification method based on hierarchical mixture of experts learning for detecting shadows from foreground is proposed. A hierarchical mixture of MLP experts method (HMME) with semi-supervised teacher-directed learning (SSP-HMME) is used. It contains a two-level mixture of experts (ME) system. The main superiority of this method is that it is more robust than state-of-the-art methods in all types of indoor and outdoor environments. The robustness is against the number of light sources, illumination conditions, surface orientations, object sizes, etc., and it is estimated using accuracy rates. The video set has been collected from 7 different datasets. The results of experiments in outdoor and indoor environments show the validity of the method in the improvement on the accuracy of both detection and discrimination rate for moving shadows in video sequences. The results of the experiments show the accuracy rate of 89 % in average in different indoor and outdoor environmental conditions that is about 6 % better than current state-of-the-art methods.     

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）。     

Figure-ground segmentation from occlusion.

Layered video representations are increasingly popular; see [2] for a recent review. Segmentation of moving objects is a key step for automating such representations. Current motion segmentation methods either fail to segment moving objects in low-textured regions or are computationally very expensive. This paper presents a computationally simple algorithm that segments moving objects, even in low-texture/low-contrast scenes. Our method infers the moving object templates directly from the image intensity values, rather than computing the motion field as an intermediate step. Our model takes into account the rigidity of the moving object and the occlusion of the background by the moving object. We formulate the segmentation problem as the minimization of a penalized likelihood cost function and present an algorithm to estimate all the unknown parameters: the motions, the template of the moving object, and the intensity levels of the object and of the background pixels. The cost function combines a maximum likelihood estimation term with a term that penalizes large templates. The minimization algorithm performs two alternate steps for which we derive closed-form solutions. Relaxation improves the convergence even when low texture makes it very challenging to segment the moving object from the background. Experiments demonstrate the good performance of our method.     

基于YC_bC_r颜色空间的背景建模及运动目标检测

高斯混合模型广泛应用于基于背景建模的运动目标检测中。首先在YCbCr颜色空间采用自适应高斯混合模型对背景的每个像素建模;然后,对输入的当前帧图像的每一像素值与该像素点对应的高斯混合背景模型的各个高斯模型进行比较,将前景运动区域（包括运动目标、投射阴影）从场景中提取出来;最后,采用局部二元图（Lo-cal Binary Pattern,LBP）来提取纹理特征,利用背景在阴影覆盖前后的纹理相似性去除投射阴影,同时结合阴影的空间几何特性优化运动目标检测结果。实验结果表明,该算法能有效地检测出投射阴影和运动目标,具有较高的实际应用价值。