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1、西安电子科技大学硕士学位论文智能视频监控系统中运动目标的检测与跟踪姓名:曹朋朋申请学位级别:硕士专业:计算机应用技术指导教师:刘志镜20100101乥 乥 (1 佪 催 YUV买 催 (2 买 Kalman 买 买 催买 乥AbstractThe detection and tracking of moving targets in video image sequences is the key technologies to implement an intelligent video surveillance. It is also a long-standing research fo

2、cuses in computer vision. The main work and contributions in the thesis includes the following two aspects:(1 On the research of the moving objects detection, we firstly improved the update strategies of background model based on the analysis of the time difference method and background subtraction

3、method.The update region and update time are limited. So the background will be updated for special purpose.Secondly by improving and optimizing the relative parameters of the Gaussian Mixture Model,moving targets will be effectively detected in complex scenes. Lastly, a fast shadow suppression algo

4、rithm in YUV color space was proposed, and the accuracy of the moving target detection was improved.(2 On the research of the moving objects tracking, based on the moving target detection,a cost function is constructed using the centroid,area and color histogram of the target.And the tracking of mul

5、tiple moving targets is realized through Kalman filter. With the motion detection results as the mask image, the color distribution of the foreground is calculated using the idea of image pyramid.The computing speed of color histogram was improved.Moreover, the PTZ(Pan/Tilt/Zoomwill be turned automa

6、tically based on a single target tracking in practical use.Thus an object can be tracked continuously.The practical result of the system shows that the proposed algorithms meet the needs of real-time and have a good detection and tracking results.Keyword:Moving Target Detection Object TrackingKalman

7、 Filter Intelligent Video Surveillance 1乥 乚 乬1 乬 乍乍“ ”(D9* 乬 乥 1.1乬 乥 乥 乥 2 80 乥 3乥 乥 乚 乥 4 乥 乥 5 乛 乥 乥 乬 乥 7 乥 乥 (1 乥 (2 乘 乘 乘 乥 2(3 (4 PTZ (Pan/Tilt/Zoom 乥 (5 乘 乘 乘 (6 (7 (1 24x7 (2 乘 IP (LAN/WAN/Internet乘乥 (3 催 乘(4 乥 乥 乥 乬 剕 1.2 乥 乥 乥 乥 乍 乬 CV I U(Computer Vision and I mage Understanding ICCV(Int

8、ernational Conference on Computer Vision 乬 乚 1996 1999 催 (DARPA 3 SARNOFF 乥 VSAM(Visual Surveillance and Monitoring6VSAM 乥 2000 Maryland Haritaoglu W48 W4Who(When( Where( What(W4 2005 偠 MA TLABCOCOA 乥 9 MA TLAB 乥 乥 亲 偠 2002 “ 乥 2003 “ 乥 催 乥 乥 乥 乥 乘 偸 乘 乥 乘 乥 乘 催 乘 乘 (1 乥 佪乬 Ahmed ElgammalDavid Harwo

9、od Larry Davis 494乥 乥 W4 (2 Wren50 W4 Rosales Sclaroff6 Kalman 乘 乬1.3 乥 乥 佪 乥 乥 催 :1. 催 佪 作 催 催 催 偠 催 5 2. 买 Kalman 买 买 Bhattacharyya 买 买 买 催 1.4 乥 乥 乬 乥 乍 乥 佪 催 催 Kalman Kalman 偠 乥 7 乥 乥 乥 乬 乥 2.1 乥 佪 乥 催 乥 乥 ( 乥 2.1.1 催 10 1. (Temporal Difference 11 ( 2. (Background Subtraction 乥 乘 乏 乬 2.1.23.催 (G

10、aussian Mixture Model催 Stauffer Grimson1999 催 催 催 催 催 催 催 4. (Optical Flow Gibson 1950 乥 16 乥 17 19 佪 乱 催 2.1.2 乥 1. N 乥 1101(,(,N t t i i B x y F x y N ¦ (2-1 N 乥 (Running average 1(,(,(1(,t t t B x y F x y B x y D D (2-2D 01D 2. 乥 N 乥 乥 1x 2x N x N N N 5 7111(,(,(,t t t t N B x y med i a nI x

11、 y I x y I " (2-33. W 4 22 乥 (,M x y (,N x y (,D x y 3 |(,(,|(,t M x y I x y D x y ! |(,(,|(,t N x y I x y D x y ! (2-4 4. 乘(Linear Predictive23 乘 Toyama Wiener 乘 乘 催 催 5. (non-parametric model ( kernel density estimatorsElgammal N 乥 1I 2I N I 催 (Gaussian Kernel t I (t f I 乘 11(t t i i t N f I

12、u K u I L ¦ (2-5 5 买 N (2-5 (KDE 催 催 催 偠 2.2 (position (velocity (shape(texture (color 催 2.2.1 乥 乥 (1 乥 乬 乥 乱 乬 催 乥 (2 乥 8 (3 乥 乬 (4 乥 乬2.2.2 乥 乘 乬 乬 乬 1. (Kalman Filter25,26 催 乘 乘 乥 2. (Partial Filter 27 偠 乥 13 剕 偠 乥 3. (Dynamic Bayesian Network 28 乘 乥 2.2.3 乘 1. 佪 乘 乘 乘 偠 29 30 31 2. 32 乘 14乥

13、 乥 催 3. 33 : (Snake Hausdorff Snake34 偠 Snake Snake 佪 Snake 乏 Snake Hausdorff Hausdorff 乥 乥 35 乥 15 4. 买 Polana Nelson 催剕 乚 Jang Cho36 乘 2.3 佪 乥 17 乥 佪 催 乘 3.1 3.1 偸3.1买 乘 3.1.1买买 乥 买 买 RGBHSV YCrCb1. RGB买 (RGB RGB RGB RGB 买 买 2. HSV买HSV买 买 佅 HSV(HueSaturationV alue买 V H S 买 3.YCrCb买乥 18YCrCb YUV 乥

14、咥 Y U V 买 乥 RGB 乥 YUV 乥 Kumar 偠 37RGB HSV YCrCb 买 乥 乥 乥 YCrCb 乥 YCrCb YCrCb 买 YCrCb RGB 0.2990.1480.615Y U V=R G B 0.5870.2890.5150.1140.4370.100 ªº«» «»«» ¬¼111R G B=Y U V 00.39 2.031.140.580ªº«» «»«» ¬&#

15、188;3.1.2 乘 乘 催 乘 乘 催 乘 乥 乥 乘 乘 催 乘 催 乘 催 39 催 催 催 22(2,i j g i j e V (3-1 V 催 催 (1 催 (2 催 催 (3 催 乥 催乥 (4 催 V V 催 乥 V ( ( (5 催 催 佪 催 催 催 催 1100,m n k l g i j f i j g k l f i k j l u ¦¦222(11200,k l m n k l ef i k j l V ¦¦2222112200,k l m n k l e e f i k j l V V ¦¦ (3-2

16、乥 乘 催 乘 222222(11200(11200,i k j l n m k i i k j l n m k i f k l eh i j eV V ¦¦¦¦ (3-3i j i j (,f k l (,h i j m n 3×35×5V 3×32V 乘 3.2 (a (b 催 3.2 乘 3.2 乥 乥 催 乍 催 3.2.1 乥 3.3 3.3 t 1t t D 1(,|(,(,|t t t D x y f x y f x y (3-4 (,t D x y (,t D x y (3-5 1(,(,0(,t t t D x y T M x y D x y T !­ ®d ¯ (

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