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A Method of Moving Objects Detection and Tacking for Video Image

Author: WangYaFei
Tutor: YinYong
School: Chongqing University
Course: Signal and Information Processing
Keywords: Moving target detection and tracking Gaussian model Spatial neighborhood correlation Kalman filter Template matching search
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 278
Quote: 2
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Abstract


Moving target detection and tracking in video sequences in video image processing, computer vision, pattern recognition and artificial intelligence and other areas of research topics is an involved, there is a wide range of applications in the areas of commercial and military, in recent years has been is people research focus. However, because the video image itself is the inherent complexity, it is to propose a robust and accurate algorithm is still a challenging work. The study focused on the case of a static scene moving target detection and tracking technology. The first main target detection and summarized, analyzed the advantages and disadvantages of each; background Gaussian model-based target detection algorithm, a more detailed analysis and exposition, and some algorithms improvements: this article on the mean and variance of the different update rates, given the number of matches a model to determine the coefficient of variance update algorithm to solve the problem of slow convergence of traditional algorithms in variance to increase the stability of the model and adaptability to the environment. Traditional detection algorithm based on Gaussian mixture model only modeling of each pixel in the image, while ignoring the correlation between neighboring pixels, target detection accuracy is not high. Therefore, a target based on spatial correlation detection algorithm, the algorithm by redefining the potential energy function of Markov random field, into the spatial neighborhood information, and obtain for adaptive threshold detection of moving targets. Experimental results show that the proposed method has better adaptability to complex scenes, it is possible to obtain more accurate test results. In moving target tracking, in order to improve the real-time nature of the algorithm, this paper uses a moving target tracking algorithm based on Kalman filter, first with the approximate area of ??the Kalman filter to predict the target goals match, and then in the forecast area. In order to adapt to the multi-objective circumstances, the connected component labeling method to extract the target features, and the establishment of a linked list of target features. And the concept of matching matrix tracking process is divided into new target, the target disappeared, object occlusion, the separation of the target, and the target matching five situations and do the analysis and research, gives the algorithm flow diagram; Finally on The target matching search algorithm to do a fairly detailed, with a cross to the goal matching search, and further improve the real-time nature of the algorithm. The simulation results show that the algorithm used for multi-target better adaptability ideal track results.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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