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cs401r_w2016:lab7 [2016/02/19 06:14] admin [Pre-requisite:] |
cs401r_w2016:lab7 [2018/02/23 17:24] wingated |
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For the first dataset, you will perform simple target tracking using the random accelerations model. You should use the equations given in Lecture 17. The data can be found here: | For the first dataset, you will perform simple target tracking using the random accelerations model. You should use the equations given in Lecture 17. The data can be found here: | ||
- | [[http://hatch.cs.byu.edu/courses/stat_ml/kfdata.mat|Simple target tracking data]] | + | [[https://www.dropbox.com/s/11fgx6e5atfv3zy/kfdata.mat?dl=0|Simple target tracking data]] |
There are two arrays in this .mat file: ''data'' contains the actual (noisy) observations at each timestep, while ''true_data'' contains the true x,y coordinates at each timestep. Note that your kalman filter should only use the noisy observations; the true x,y coordinates are only given for visualization purposes. | There are two arrays in this .mat file: ''data'' contains the actual (noisy) observations at each timestep, while ''true_data'' contains the true x,y coordinates at each timestep. Note that your kalman filter should only use the noisy observations; the true x,y coordinates are only given for visualization purposes. | ||
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The dataset is derived from a sequence of images, which can be downloaded here: | The dataset is derived from a sequence of images, which can be downloaded here: | ||
- | [[http://hatch.cs.byu.edu/courses/stat_ml/ball_data.mat|Ball data]] | + | [[https://www.dropbox.com/s/1x7jtg162upc3o9/ball_data.mat?dl=0|Ball data]] |
The data is a sequence of images of a ball rolling across a camera's field of view. We'd like to implement a tracker for the ball, but there's no way we can cope with images in the framework of the Kalman filter. Instead, we'll do a brief pre-processing step: we'll use template matching to search for the ball in each frame of video, and record the best ''(x,y)'' position of the template match. | The data is a sequence of images of a ball rolling across a camera's field of view. We'd like to implement a tracker for the ball, but there's no way we can cope with images in the framework of the Kalman filter. Instead, we'll do a brief pre-processing step: we'll use template matching to search for the ball in each frame of video, and record the best ''(x,y)'' position of the template match. |