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Study On Moving Object Detection And Tracking In Video Sequences

Posted on:2013-10-24Degree:DoctorType:Dissertation
Country:ChinaCandidate:B LiFull Text:PDF
GTID:1228330395467902Subject:Signal and Information Processing
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Moving object detection and tracking is one of the hotest research topic in computer vision field. It is the basic and key technology of human-computer interaction, intelligent control, and visual navigation. Moving object detection is a process of finding motion in image sequences using color, edge, texture, space and other aspects of differences, extracting the shape of object, locating the object’s coordinates, size, speed etc., and tracking it. However, in real environment, it is very difficult to detect the moving object accurately and robustly, because of shadow, illumination change, complex background, objective change and occlusion.Video based motion detection and tracking technology has been studied and discussed in this thesis. For some difficulties in the fourth-generation human-computer interaction, we introduced some new algorithms to resolve them. The main contributions of the dissertation are as follows:(1) To remove moving shadow, we present a novel approach of moving shadow elimination based on Shadow Flow and maximum a posteriori probability of3D Markov Random Field (3D MAP-MRF) by integrating motion detection and shadow elimination.1) Gaussian Mixture Model is built as background model for each pixel. By comparing current pixel and GMM, we classify candidate shadow pixel through a weak shadow classifier and send it to Shadow Flow Model. Which isbuilt through the online learning of candidate shadows coming from weak classifier.2) A3D MRF is constructed of GMM, Shadow Flow and current images. We proved that our new energy function meets F2theorem, so that our model is "graph representable", and can be solved with graph cuts optimally.3)A3D graph is constructed according3D MRF. A dynamic graph cuts algorithm is used to find the min-cut/max-flow, which is equal to the maximum posteriori probability of labels. Each pixel is assigned by "foreground" and "non-foreground" label, and moving object detection with shadow elimination is completed. Experiments show that our algorithm can eliminate the moving shadows and get accurate results.(2) For complex dynamic scenes, we present an approach to segment moving objects with nonparametric estimated cumulative local kernel histogram (NPE-CLKH). Texture information of surrounding pixels is integrated into cumulative local kernel histogram. Experiments show that our algorithm can detect motion robustly in complex dynamic scenes containing the rippling water, leaves shaking etc..By using the correlation and texture of spatially proximal pixels, a local kernel histogram background model is constructed. Cumulative local kernel histogram of the corresponding pixel of N frames is computed. Then probability distribution of cumulative local kernel histogram is estimated with nonparametric techniques. We employ Bhattacharyya distance to measure the similarity of local kernel histogram between estimated background model pixel by pixel in current frame, and compare CLKH of Neighboring pixel, to reduce the impact of dynamic texture, and camera shaking. Our approach can reduce false detections due to disturbing noise and small motions in dynamic scenes. This approach can deal with complex dynamic texture problem, and get robust results.(3) We present a novel texture-based algorithm to detect motion with codebook and Gaussian local binary patterns (GLBP), which can get texture background model on-line, and resolve the problems of complex background, slight illumination changes etc..Firstly, a codebook model is constructed in similar manner of pixel clustering. Distribution of background pixels is represented code words cluster according to using the color and brightness similarity between codebook and current pixel. Our algorithm updates the codebook model both in initial step and detection step to deal with changes of background pixels. A single Gaussian model of pixel-wise is used to build the pixel’s value change model on-line. A background model based on Gaussian local binary patterns is constructed on-line by applying the correlation and texture of spatially proximal pixels. Finally current image is segmented into two parts, foreground and background by fusing those features. Experiments show that our algorithms achieve good detection results.(4) To deal with the issue of target change and occlusion, we propose a novel adaptive model update method for real-time mean shift blob tracking. We use adaptive LMS filter for filtering object kernel histogram and update the vectors of the object kernel histogram dynamically. New bins of target kernel histogram are added, and useless bins are removed. The target kernel histogram can reflect the target changes, and better matchs the target model. Experiments show that our method gets accurate and robust tracking results in the case of target change and occlusion.In this thesis, corresponding solutions are proposed to reslove the problems of shadow, dynamic complex scenes, target change and occlusion. Experiments demonstrate that our algorithms achieve good results.
Keywords/Search Tags:motion detection, Shadow Flow, 3D MRF, local kernel histogram, codebook, object tracking, mean shift
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