1. 1 2012 IEEE International Conference on Multimedia and Expo

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3. Limitations a) object tracking is limited to the image plane, not in the physical world [4]. b) it assumes static background [1]. c) it incurs high computational complexity.[6] [1] R. Rosales and S. Sclaroff, “3D Traject Recovery for Tracking Multiple Objects and Trajectory Guided Recognition of Actions,” [4] S. Weng, C. Kuo and S. Tu, “Video object tracking using adaptive Kalman filter,” [6] M. Roh, T. Kim, J. Park and S. Lee, “Accurate object contour tracking based on boundary edge selection” 3

4. Light-weight computing due to the limited computation power of smartphones, complex algorithms requiring high time complexity and space complexity will not fit. Reasonable accuracy to estimate a remote target position based on video, an accurate tracking with the object boundary appropriately identified is very important. Interactive user interface Input from user’s interaction is a unique feature on smartphones and is very useful for video tracking. 4

5. [13] D. F. Dementhon and L. S. Davis, “Model-based object pose in 25 lines of code,” 5

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10. Optical Flow Tracking [18]Patch Classification [18] B. D. Lucas and T. Kanade, “An interactive image registration technique with an application to stereo vision”. 10

11. shows the moving object’s optical flow features. 11 Variance (noise)

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15. L ’s gray scale intensity value at frame t motion compensated prediction residual errors 15

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23. Kalman gain Correct State prediction Covariance prediction Prediction Correction 23 noise measurement error Observation model Observation matrix

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