Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> a: SPATIAL METHODS <s> Texture and color are two primitive forms of features that can be used to describe a scene. While conventional local binary pattern (LBP) texture based background subtraction performs well on texture rich regions, it fails to detect uniform foreground objects in large uniform background. As such, color information can be used to complement texture feature. In this study, we propose to incorporate local color feature based on Improved Hue, Luminance, and Saturation (IHLS) color space and introduce an adaptive scheme that automatically adjusts the weight between texture and color similarities based on the pixel's local properties: texture uniformity and color saturation. Experiments on eight challenging sequences demonstrate the effectiveness of the proposed method compared to the state-of-the-art algorithms. <s> BIB001 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> a: SPATIAL METHODS <s> Moving camera, such as PTZ (pan-tilt-zoom) camera, has been widely applied in visual surveillance system. However, it's difficult to extract moving objects because of the dynamic background caused by the camera motion. In this paper, a novel framework for moving object segmentation exploiting two cameras collaboration is presented by combining background subtraction and image registration method. The proposed method uses one static camera to capture large-view images at low resolution, and one moving camera (i.e. PTZ camera) to capture local-view images at high resolution. Different with methods using a single moving camera, the moving objects can be effectively segmented in the static camera image by background subtraction method. Then image registration method can be applied to extract moving region in the moving camera image. To deal with the resolution and intensity discrepancy between two synchronized images, we design a practical three-step image registration method, which has higher registration accuracy than traditional feature based method. Experimental results on outdoor scene demonstrate the effectiveness and robustness of proposed approach. <s> BIB002 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> a: SPATIAL METHODS <s> Gaussian mixture models(GMM) is a widely used approach for background modeling. However, computational barriers have limited its usage in real-time video processing applications. In this paper, we discussed a new update algorithm to achieve the goal of fast detection. Dirichlet prior are introduced to avoid redundant Gaussian components, reducing the computation time of each pixel. Most of the existing GMM based techniques use background/foreground data proportion, which is highly sensitive to the environment, to detect object. To avoid its possible negative effects on segmentation, we use sigmoid function to approximate the probability of Gaussian component belongs to the background and set a threshold for it to segment. Experimental results show this method leads to a faster and a better segmentation than traditional GMM. <s> BIB003 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> a: SPATIAL METHODS <s> The detection of moving objects in videos is very important in many video processing applications, and background modeling is often an indispensable process to achieve this goal. Most of the traditional background modeling methods utilize color or texture information. However, color information is sensitive to illumination variations and texture information cannot be utilized to separate smooth foreground from smooth background in most cases. Achieving good performance in terms of high foreground detection accuracy and low computational cost is also challenging. In this paper, we propose a new integration framework of texture and color information for background modeling, in which the foreground decision equation includes three parts (one part for color information, one part for texture information, and the left part for the integration of color and texture information). This framework is able to combine the advantages of texture and color features while inhibiting their disadvantages as well. Moreover, we propose a block-based method to accelerate the background modeling. In particular, in the texture information modeling process, a single histogram model is established for each block whose bins indicate the occurrence probabilities of different patterns, which is different from the traditional multihistogram model for block-based background modeling, and then dominant background patterns are selected to calculate the background likelihood of new coming blocks. Dynamic background and multimodal problems can be handled through this technique. To evaluate the foreground detection performance reasonably, a new quality measure is proposed. Extensive experiments on various challenging videos validate the effectiveness of the proposed method over state-of-the-art methods. <s> BIB004 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> a: SPATIAL METHODS <s> A particular algorithm for moving object detection using a background subtraction approach is proposed. We generate the background model by combining quad-tree decomposition with entropy theory. In general, many background subtraction approaches are sensitive to sudden illumination change in the scene and cannot update the background image in scenes. The proposed background modeling approach analyzes the illumination change problem. After performing the background subtraction based on the proposed background model, the moving targets can be accurately detected at each frame of the image sequence. In order to produce high accuracy for the motion detection, the binary motion mask can be computed by the proposed threshold function. The experimental analysis based on statistical measurements proves the efficiency of our proposed method in terms of quality and quantity. And it even outperforms substantially existing methods by perceptional evaluation. <s> BIB005 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> a: SPATIAL METHODS <s> Moving object detection is an essential component for security video surveillance system and other computer vision applications. Although the latest object detection methods get promising detection expectations, however, accurate detection is still a tricky problem due to various challenges such as aperture effects, illumination variations, camouflage issues and retention problems in unconstrained video environments. In this paper, we propose a brand-new theoretical framework for foreground object detection based on the stable spatial relationship between current pixel and the randomly selected pixels in current frame. Different from the existing methods which determine the moving object area by comparing each pixel value with its surrounding pixels or by comparing two pixel values occupying the same positions in adjacent frames, the proposed algorithm sets up a spatial sample set for each individual pixel and defines Spatial Sample Difference Consensus (SSDC), which denotes changes of stable spatial relationship rather than direct changes in pixel values. Thus, the proposed algorithm computes the SSDC between two adjacent frames to subtract the moving objects. The experiments on recent data-set in both indoor and outdoor surveillance video sequences show that the proposed method achieved promising performance after compared with several state-of-the art methods. <s> BIB006
The representative spatial background subtraction methods include Yokoyama and Poggio  , Chua et al. BIB001 , Cui et al. BIB002 , Han et al. BIB004 , Song et al. BIB003 , Elharrouss et al. BIB005 , and Zhang et al. BIB006 . For example, Yokoyama and Poggio  developed a contour-based background subtraction method for moving object detection and tracking. This method is performed using lines computed from a gradient-based optical flow and an edge detector. The edges extracted from optical flow and edge detector are stored as lines. The background lines of the previous frame are subtracted to get the contour of objects. Chua et al. BIB001 introduced an adaptive texture-color based background subtraction method for moving object segmentation in video surveillance. It incorporates the Improved Hue, Luminance, and Saturation (IHLS) color space based local color feature with local binary pattern (LBP) texture for more robust background subtraction. Han et al. BIB004 developed an integration of color and texture framework for robust moving object detection, where a block-based background modeling is utilized to accelerate background subtraction process. Song et al. BIB003 utilized a widely-used background subtraction algorithm Gaussian mixture models (GMM) and a Dirichlet prior based acceleration strategy for fast moving object detection. To deal with the illumination changes, Elharrouss et al. BIB005 proposed a particular algorithm for moving object detection based on combining quad-tree decomposition with entropy theory. The background model is constructed by combing the quad-tree decomposition with entropy theory, which is insensitive to sudden illumination change in the scene. Zhang et al. BIB006 developed a moving object detection and segmentation algorithm using pixel spatial sample difference consensus.