Zhaoxia Fu, Yan Han Measurement Volume 45, Issue 4, May 2012, Pages 650–655 Reporter: Jing-Siang, Chen

 Introduction  Methods background subtraction algorithm Kalman filter Centroid weighted method  Experimental results  Conclusions  Personal Remark 2

 Visual tracking is an important research direction of visual applications, it is a combination of image processing.  Detect moving target from obtaining the images, position the target, analyze the characteristics of the location and execute real-time tracking. 3

 The Kalman filter is an effective state estimation method, which uses the state model to predict the target state, and combines the observation model to estimate the posterior probability density function of the state.  The idea of the Kalman filter was widely used after introduced to visual object tracking, but when there is a strong interference, the Kalman filter will be ineffective. 4

 In this paper, this algorithm of centroid weighted Kalman filter (CWKF) for objec tracking is proposed.  1. Uses background subtraction algorithm to detect moving target region, then uses the Kalman filter to predict the location of the object tracking. 5

 2. Centroid weighted method to optimize the predictive state value for further updating and observation.  3. Measurement updating equations completes ultimate estimate location of object tracking. 6

 Background Subtraction is basic idea is to subtract a background image from the current frame and to classify each pixel a foreground or background by comparing the difference with a threshold. 7

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 Updates the background using the following method: (2) 9

 The Kalman filter can be used to solve linear, Gaussian state estimation problem.  Trying to estimate the state s € R of a discrete-time controlled process in which transition is from t to t + 1 can be expressed with the equation. (3) (4) 10

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 If the centroid distributions of the pixels of the same gray level are seen as random variables, the mathematical expectation of the centroid position is given by (12) 14

 If the probability of the pixels is bigger, the weighted value of the centroid position is also bigger, the position of the target is express by (13) 15

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 The centroid weighted Kalman filter process can be given by the following steps: 1. make frame average. 2. utilize background subtraction method to detect moving object. 3. update dynamically the background image using Eq.(2). 4. update the expected mean ŝ t+1|t using Eqs. (13)–(15), method, and obtain a new expected mean ŝt+1|t. 5. calculate the measurement of z t+1 and Kalman gain matrix of K t+1, by the new expected mean ŝt+1|t,and obtain a new ŝ t+1|t+1 using Eq. (7). 6. go the second step for tracking at the next frame. 17

 After the centroid weighted method, the expected mean ŝ t+1 | t is closer to the true trajectory. Therefore, doing linear processing to the updated expected mean ŝ t+1|t for obtaining the measure of ŝ t+1|t+1 that is as the reference will be more accurate. 18

 We set this system’ state transition matrix and measurement matrix. 19

 Fig. 1 is the tracking process of moving object. Fig. 1. The tracking process of moving object: (a–f) frames 11, 17, 23, 30, 36,58. 20

 Let us set up the coordinates system with upper left corner of video images as an origin. Fig. 2. Comparing the predictive values of Fig. 3. Comparing the predictive values of the centroid in the x-direction. the centroid in the y-direction. 21

 This paper proposes an algorithm based on centroid weighted Kalman filter (CWKF) for object tracking.  Use centroid weighted method to optimize the target position for further enhancing tracking accuracy. 22

 If experimental background changes to be more complicated,…? 23

Thanks for your attention 24