cs401r_w2016:lab7
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cs401r_w2016:lab7 [2016/02/18 05:20] admin |
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| ====Pre-requisite:==== | ====Pre-requisite:==== | ||
| - | You must install the python ''skimage'' package. This can be installed via ''conda install skimage''. | + | You must install the python ''skimage'' package. This can be installed via ''conda install scikit-image''. |
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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. | ||
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| # assumes that "mus" contains a list of Gaussian means, and that "covs" is a list of Gaussian covariances | # assumes that "mus" contains a list of Gaussian means, and that "covs" is a list of Gaussian covariances | ||
| + | # also assumes you are running in Jupyter Notebook | ||
| + | |||
| + | from IPython.display import clear_output | ||
| + | |||
| for t in range(0, data.shape[0]): | for t in range(0, data.shape[0]): | ||
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| plt.xlim([1, 360]) | plt.xlim([1, 360]) | ||
| plt.ylim([243,1]) | plt.ylim([243,1]) | ||
| - | | + | clear_output(wait = True) |
| plt.pause(0.01) | plt.pause(0.01) | ||
| </code> | </code> | ||
cs401r_w2016/lab7.1455772836.txt.gz · Last modified: 2021/06/30 23:40 (external edit)
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