感興趣運動目標檢測的研究與實現
發(fā)布時間：2018-07-31 20:50
【摘要】：目標檢測是圖像視頻分析、模式識別和計算機視覺應用的一個首要問題,在各個方面起著重要的作用,如視頻監(jiān)控、車輛導航、機器人視覺和智能交通系統(tǒng)等。一般查看視頻的時候,人們通常只會對特定的運動目標有很大興趣,可將其稱為感興趣目標。感興趣目標的準確快速提取將大大提高后續(xù)跟蹤和識別處理的有效性。在實際應用中,面對海量視頻圖像以及不同應用場合,需要解決感興趣運動目標檢測的準確性、實時性和平臺通用性問題。具體研究工作如下:假設視頻中的運動目標都是感興趣目標,基于能量泛函的圖像分割是廣泛使用的方法之一。針對現今基于能量泛函的圖像分割計算效率不高且平臺局限的特點,首先提出了連續(xù)最大流圖像分割算法在Open CL上跨平臺并行實現。在分析連續(xù)最大流算法的并行特征基礎上,將迭代求解最大流優(yōu)化問題并行實現。合理調用異構平臺的CPU和GPU,使算法具有高性能和可移植性。然后將所提算法應用于視頻,在混合高斯背景模型下,結合并行后的連續(xù)最大流圖像分割算法進行感興趣運動目標檢測。實驗結果表明,在保證視頻圖像的分割質量下,算法的GPU+CPU異構并行實現較CPU串行實現有數量級的提升;算法在AMD,Nvidia和Intel三大主流硬件平臺上通用運行,驗證了算法的有效性和平臺的可移植性,基本滿足實際應用的要求。實際視頻分析中并非對所有運動目標都感興趣,需要根據不同的應用場合提取某一類特定的感興趣運動目標。針對由于復雜背景和其他非感興趣運動目標的干擾而導致不能準確檢測出感興趣運動目標的問題,本文提出了一個新的感興趣運動目標檢測框架。在傳統(tǒng)的馬爾科夫隨機場(Markov Random Field,MRF)基礎上,引入haar-like級聯(lián)分類器搜索到的感興趣建議區(qū)域作為高階勢能項,從而構建高階MRF模型。在這統(tǒng)一能量框架下,通過降階優(yōu)化最終轉為用最大流/最小割解決能量最優(yōu)化問題。實驗結果表明,引入haar-like特征訓練的級聯(lián)分類器搜索得到的感興趣建議區(qū)域作為高階勢能項進行建模,提高了模型的表達能力;同時算法有效增強了感興趣目標的分割效果,尤其是使感興趣目標有更好的邊緣性,提高分割的準確度,改善分割感興趣目標的視覺效果。
[Abstract]:Target detection is one of the most important problems in image video analysis, pattern recognition and computer vision applications. It plays an important role in many aspects, such as video surveillance, vehicle navigation, robot vision and intelligent transportation system. When viewing a video, people usually only have a great interest in a particular moving object, which can be called the object of interest. Accurate and fast extraction of objects of interest will greatly improve the effectiveness of follow-up tracking and recognition processing. In practical applications, in the face of mass video images and different applications, it is necessary to solve the problems of accuracy, real-time and platform generality of moving object detection of interest. The research work is as follows: assuming that the moving targets in video are all objects of interest, image segmentation based on energy functional is one of the widely used methods. In view of the low efficiency and limited platform of image segmentation based on energy functional, a continuous maximum flow image segmentation algorithm is proposed in this paper, which is implemented in parallel on Open CL platform. Based on the analysis of the parallel characteristics of the continuous maximum flow algorithm, the iterative solution to the maximum flow optimization problem is implemented in parallel. The algorithm has high performance and portability by reasonably calling CPU and GPU of heterogeneous platform. Then, the proposed algorithm is applied to video, and combined with parallel continuous maximum flow image segmentation algorithm in hybrid Gao Si background model to detect moving objects of interest. The experimental results show that the GPU CPU isomerous parallel implementation of the algorithm is of the order of magnitude higher than that of the CPU serial implementation, and the algorithm is running on the three main hardware platforms of Intel and AMD-Nvidia under the guarantee of video image segmentation quality. The validity of the algorithm and the portability of the platform are verified. Not all the moving targets are interested in the actual video analysis, so it is necessary to extract a certain kind of moving objects according to different applications. In view of the problem that the moving objects of interest can not be detected accurately due to the interference of complex background and other moving objects of non-interest, a new frame for detecting moving objects of interest is proposed in this paper. Based on the traditional Markov Random Field (Markov Random), the region of interest found by the haar-like cascade classifier is introduced as a high-order potential energy term, and the higher-order MRF model is constructed. In this unified energy framework, the reduced order optimization is eventually converted to the maximum flow / minimum cut to solve the energy optimization problem. The experimental results show that the proposed region of interest obtained by cascaded classifier based on haar-like feature training is modeled as a high-order potential energy item, and the expression ability of the model is improved, and the segmentation effect of the object of interest is effectively enhanced by the algorithm. Especially, it can improve the edge of the object of interest, improve the accuracy of segmentation and improve the visual effect of the object of interest.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
本文編號：2156788
[Abstract]:Target detection is one of the most important problems in image video analysis, pattern recognition and computer vision applications. It plays an important role in many aspects, such as video surveillance, vehicle navigation, robot vision and intelligent transportation system. When viewing a video, people usually only have a great interest in a particular moving object, which can be called the object of interest. Accurate and fast extraction of objects of interest will greatly improve the effectiveness of follow-up tracking and recognition processing. In practical applications, in the face of mass video images and different applications, it is necessary to solve the problems of accuracy, real-time and platform generality of moving object detection of interest. The research work is as follows: assuming that the moving targets in video are all objects of interest, image segmentation based on energy functional is one of the widely used methods. In view of the low efficiency and limited platform of image segmentation based on energy functional, a continuous maximum flow image segmentation algorithm is proposed in this paper, which is implemented in parallel on Open CL platform. Based on the analysis of the parallel characteristics of the continuous maximum flow algorithm, the iterative solution to the maximum flow optimization problem is implemented in parallel. The algorithm has high performance and portability by reasonably calling CPU and GPU of heterogeneous platform. Then, the proposed algorithm is applied to video, and combined with parallel continuous maximum flow image segmentation algorithm in hybrid Gao Si background model to detect moving objects of interest. The experimental results show that the GPU CPU isomerous parallel implementation of the algorithm is of the order of magnitude higher than that of the CPU serial implementation, and the algorithm is running on the three main hardware platforms of Intel and AMD-Nvidia under the guarantee of video image segmentation quality. The validity of the algorithm and the portability of the platform are verified. Not all the moving targets are interested in the actual video analysis, so it is necessary to extract a certain kind of moving objects according to different applications. In view of the problem that the moving objects of interest can not be detected accurately due to the interference of complex background and other moving objects of non-interest, a new frame for detecting moving objects of interest is proposed in this paper. Based on the traditional Markov Random Field (Markov Random), the region of interest found by the haar-like cascade classifier is introduced as a high-order potential energy term, and the higher-order MRF model is constructed. In this unified energy framework, the reduced order optimization is eventually converted to the maximum flow / minimum cut to solve the energy optimization problem. The experimental results show that the proposed region of interest obtained by cascaded classifier based on haar-like feature training is modeled as a high-order potential energy item, and the expression ability of the model is improved, and the segmentation effect of the object of interest is effectively enhanced by the algorithm. Especially, it can improve the edge of the object of interest, improve the accuracy of segmentation and improve the visual effect of the object of interest.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
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