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  本文選題：多攝像機跟蹤　切入點：目標交接　出處：《聊城大學》2015年碩士論文　論文類型：學位論文

【摘要】：基于視頻監(jiān)控的運動目標跟蹤技術屬于人工智能的分支,涉及到模式識別、圖像處理、計算機視覺、機器學習等學科。目前,單攝像機的目標檢測和跟蹤技術發(fā)展的比較成熟,取得了大量的研究成果。但是,仍有一系列的難題需要解決。比如,目標之間出現(xiàn)遮擋、復雜背景的干擾、動態(tài)背景情況的跟蹤等。多攝像機目標跟蹤是近年來的研究熱點,而目標交接是實現(xiàn)目標匹配和場景切換跟蹤的關鍵技術,是多攝像機監(jiān)控必須解決的難題。本課題在研究單攝像機運動目標檢測、跟蹤算法基礎上提出了新的方法,并重點研究了雙攝像視野重疊情況下的視野分界線生成和目標交接算法,主要工作如下:1.采用了融合的目標檢測算法。首先采用幀間差分判斷出目標所在位置的大致范圍,并鎖定中心位置,然后在此范圍附近,采用背景差分的方法,準確判斷目標區(qū)域。采用此種方法的優(yōu)點是,對目標區(qū)域的判斷準確,且受外界擾動的影響較小,可以得到較為完整的目標前景區(qū)域。2.采用了粒子濾波改進的Camshift目標跟蹤算法,結合Camshift算法對粒子濾波算法進行改進。同時解決了粒子濾波需要計算大量粒子,粒子收斂速度慢,跟蹤實時性差問題和遮擋情況下Camshift算法目標跟蹤魯棒性低問題。3.提出了基于SURF快速特征點匹配和投影不變量的視野分界線生成算法。首先利用SURF算法快速獲得相鄰兩個攝像機中帶有重疊區(qū)域的背景圖像的特征點,然后利用RANSAC算法對特征點向量進行優(yōu)化,去除匹配誤差較大的點。通過對RANSAC算法的迭代,選擇出4對最佳匹配點,最后,根據(jù)兩幅背景圖像的邊界點以及投影不變量求兩幅圖像中重疊區(qū)域,并進行標定,生成視野分界線。4.提出了基于視野分界線幾何坐標變換曲線匹配方法的目標交接算法。首先實時檢測公共視野區(qū)域內是否有運動目標出現(xiàn),然后進行坐標變換,并在新的坐標系下,對目標像素進行統(tǒng)計,最后,將得到的像素統(tǒng)計曲線進行匹配和標定。
[Abstract]:The branch target video surveillance tracking technology is based on artificial intelligence, involves pattern recognition, image processing, computer vision, machine learning and other disciplines. At present, the target detection and tracking technology development of single camera is relatively mature, made a lot of research results. However, there are still a series of problems need to be solved. For example between, target occlusion, complex background and dynamic background tracking. Multi camera tracking is a hot research topic in recent years, but the goal is to achieve the goal of key technology transfer, and scene change tracking, multi camera surveillance is a difficult problem to be solved. This topic in the study of single camera moving target detection, tracking the algorithm is proposed based on the new method, and focuses on the dual camera view overlap under the vision line generation and target handoff algorithm, the main work is as follows 1.: the target detection algorithm fusion. Firstly, using frame difference estimate approximate range of target position, and the locking range near the center position, and then, using the background difference method, accurately determine the target area. The advantages of this method is that the target area is accurate, and less influence by the external disturbance, the target tracking algorithm can get more complete foreground.2. using the improved particle filter Camshift, combined with Camshift algorithm to improve particle filter algorithm. At the same time to solve the particle filter need to calculate a large number of particles, the particles slow convergence of Camshift algorithm and target occlusion tracking real-time robust tracking problems the problem of low.3. proposed SURF fast feature point matching and projective invariants based on the view of demarcation line generation algorithm. SURF algorithm is first used to obtain fast The background image feature points with overlap area of two adjacent the camera, and then use RANSAC algorithm to optimize feature vector, remove the matching error larger. Through the iteration of the RANSAC algorithm, selected 4 of the best matching point, finally, according to the overlap area of the two painting like boundary points and projective invariants for the two images, and calibrate the generated vision line.4. proposed target handoff algorithm vision line geometric coordinate transformation curve matching method based on real-time detection. The first public view area if there is a moving target, and then the coordinate transformation, and in the new coordinate system, statistics, on target pixel finally, the statistics of pixel curve obtained by matching and calibration.

【學位授予單位】：聊城大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.41;TN948.6

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本文編號：1615172