基于空間鄰域約束編碼的視頻目標跟蹤研究
發(fā)布時間：2018-12-21 17:31
【摘要】：人類的偉大理想之一就是讓機器人可以具備像他們自己一樣的視覺功能。近一個世紀以來,信息技術飛速發(fā)展,計算機視覺方面更是科研工作者們研究的重點。到今天,計算機視覺領域的目標跟蹤技術在計算精確度和跟蹤實時性等方面已經(jīng)達到了較高的水平。目標跟蹤的目的,就是針對長度為不同時間的某些幀連續(xù)序列圖像,這些序列所包含的每幅圖像中均由需要被定位的運動目標。對科研工作者來說。只有符合以下標準,視頻目標跟蹤方法才能達標:1.實時性好,即其處理速度需要達到一定的數(shù)值;2.魯棒性強,即面對復雜場景或目標的姿勢、動作均發(fā)生大幅度改變時也不會影響算法的穩(wěn)定性,仍然可以跟蹤到目標。但與理論研究不同,在實際應用中,視頻目標跟蹤技術仍面對著諸如場景的復雜化,目標的突然變化,光照變化等多個難題。本論文針對類似對象干擾、動態(tài)模糊、低對比度、部分遮擋和光照變化等實際生活中出現(xiàn)在被跟蹤目標所在視頻序列的常見情況做出了分析研究,取得了一些主要研究成果,說明如下:1.提出一種基于空間鄰域約束編碼的視頻跟蹤方法。該方法采用了新的約束策略,即通過加權碼進行雙重加權的空間鄰域約束編碼模型,該模型是通過分別考慮特征像素的相鄰像素的灰度加權編碼及他們之間的歐式距離加權編碼來得到的。該模型除了考慮像素本身顏色值以外,還將距離這類空間信息考慮在內(nèi),以獲得在復雜場景的幀提取對應像素各種特征的健壯的代碼。它進一步增強了編碼的穩(wěn)定性,使目標跟蹤所使用的跟蹤器更加健壯,在進行目標跟蹤時取得了更加精確可靠的跟蹤效果。2.在空間鄰域約束編碼的基礎上,提出了其與Mean shift(均值漂移)綜合后跟蹤這樣一種視頻追蹤方式。本方法在利用空間鄰域約束編碼模型來得到目標像素準確編碼的同時,加入了Mean shift算法。Mean shift算法擁有的優(yōu)勢為:1.運算成本低,當待追蹤標的范圍確定時,能夠以24幀/秒的速率進行追蹤;2.即使目標產(chǎn)生形變,角度偏移,其邊際不完全顯示等情況,該算法也會排除干擾,準確追蹤這兩類優(yōu)勢。從而在確保算法健壯性的基礎上提高了算法的實時性,使其面對復雜的場景時也可以做到準確定位跟蹤。
[Abstract]:One of the great ideals of mankind is that robots can have the same visual function as they do. In the past century, with the rapid development of information technology, computer vision is the focus of researchers. Today, the target tracking technology in the field of computer vision has reached a high level in computing accuracy and real-time tracking. The purpose of target tracking is to target some successive sequence images of frames with different length of time. Each image contained in these sequences is made up of moving targets that need to be located. For researchers. Video target tracking can only meet the following standards: 1. Good real-time, that is, the processing speed needs to reach a certain value; 2. Robustness is strong, that is, facing the posture of complex scene or target, the stability of the algorithm will not be affected when the action changes greatly, and the target can still be tracked. However, different from the theoretical research, video target tracking technology still faces many difficulties in practical applications, such as the complexity of the scene, the sudden change of the target, the change of illumination, and so on. In this paper, we analyze and study the common situations of similar object interference, dynamic blur, low contrast, partial occlusion and illumination change, which appear in the video sequence of the target being tracked, and obtain some main research results. The description is as follows: 1. A video tracking method based on spatial neighborhood constrained coding is proposed. In this method, a new constraint strategy is adopted, that is, the spatial neighborhood constraint coding model is double weighted by weighted code. The model is obtained by taking into account the grayscale weighted coding of adjacent pixels and the Euclidean distance weighted coding between them respectively. In addition to considering the color value of pixels, the model also takes the spatial information of distance into account to obtain robust codes for extracting various features of pixels in the frames of complex scenes. It further enhances the stability of coding, makes the tracker used in target tracking more robust, and achieves a more accurate and reliable tracking effect. 2. Based on the spatial neighborhood constrained coding, this paper proposes a video tracking method which is integrated with Mean shift (mean shift. In this method, the spatial neighborhood constraint coding model is used to get the accurate coding of the target pixels, and the advantages of the Mean shift algorithm. Mean shift algorithm are as follows: 1. The operation cost is low, when the range of the target to be tracked is determined, it can be traced at the rate of 24 frames / second; 2. Even if the target produces deformation, angle deviation and incomplete display of the edge, the algorithm can eliminate the interference and track the two kinds of advantages accurately. In order to ensure the robustness of the algorithm on the basis of improving the real-time algorithm, so that it can also be accurate location tracking in the face of complex scenes.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN919.81
本文編號：2389230
[Abstract]:One of the great ideals of mankind is that robots can have the same visual function as they do. In the past century, with the rapid development of information technology, computer vision is the focus of researchers. Today, the target tracking technology in the field of computer vision has reached a high level in computing accuracy and real-time tracking. The purpose of target tracking is to target some successive sequence images of frames with different length of time. Each image contained in these sequences is made up of moving targets that need to be located. For researchers. Video target tracking can only meet the following standards: 1. Good real-time, that is, the processing speed needs to reach a certain value; 2. Robustness is strong, that is, facing the posture of complex scene or target, the stability of the algorithm will not be affected when the action changes greatly, and the target can still be tracked. However, different from the theoretical research, video target tracking technology still faces many difficulties in practical applications, such as the complexity of the scene, the sudden change of the target, the change of illumination, and so on. In this paper, we analyze and study the common situations of similar object interference, dynamic blur, low contrast, partial occlusion and illumination change, which appear in the video sequence of the target being tracked, and obtain some main research results. The description is as follows: 1. A video tracking method based on spatial neighborhood constrained coding is proposed. In this method, a new constraint strategy is adopted, that is, the spatial neighborhood constraint coding model is double weighted by weighted code. The model is obtained by taking into account the grayscale weighted coding of adjacent pixels and the Euclidean distance weighted coding between them respectively. In addition to considering the color value of pixels, the model also takes the spatial information of distance into account to obtain robust codes for extracting various features of pixels in the frames of complex scenes. It further enhances the stability of coding, makes the tracker used in target tracking more robust, and achieves a more accurate and reliable tracking effect. 2. Based on the spatial neighborhood constrained coding, this paper proposes a video tracking method which is integrated with Mean shift (mean shift. In this method, the spatial neighborhood constraint coding model is used to get the accurate coding of the target pixels, and the advantages of the Mean shift algorithm. Mean shift algorithm are as follows: 1. The operation cost is low, when the range of the target to be tracked is determined, it can be traced at the rate of 24 frames / second; 2. Even if the target produces deformation, angle deviation and incomplete display of the edge, the algorithm can eliminate the interference and track the two kinds of advantages accurately. In order to ensure the robustness of the algorithm on the basis of improving the real-time algorithm, so that it can also be accurate location tracking in the face of complex scenes.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN919.81
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