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elastic_play
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"collapsed": true | |
}, | |
"source": [ | |
"#### Problem definition:\n", | |
"\n", | |
"Given a video at duration: $ D $,\n", | |
"\n", | |
"By running a shot-detection algorithm, we get the information of n shots: $ v_1, v_2, v_3...v_n$ . (each shot length varies from 2 sec - 5 sec.)\n", | |
"\n", | |
"Based on the computer vision algorithm, we also have the score of each shot: $ s_1, s_2, s_3, ...s_n $ and the duration of each shot: $ d_1, d_2, .... d_n $.\n", | |
"\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"From the prior research, people have reported the correlation between the playback rate and the understand rate:\n", | |
"\n", | |
"Understand rate is referenced to the ratio of people can well understand the content for certain speed.\n", | |
"\n", | |
"\n", | |
"for silent parts: $ u_s = -0.1 * (v_s -1 ) + 1 $ \n", | |
"\n", | |
"for non-silent parts: $ u_n = - (v_n - 1) + 1 $" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"\n", | |
"Our preliminary study shows users prefer a universal constant speed across the video.\n", | |
"\n", | |
"Based on this finding, we maintain a universal $v_s$ and $v_n$ for all the shots.\n", | |
"\n", | |
"Meanwhile, we want to maintain an equal understanding for the silent parts and non-silent parts. \n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"As we speed up the content, the actual score ${s_k}^*$ can be perceived by the user is:\n", | |
"\n", | |
"${s_k}^* = u_s * {s_k} $\n", | |
"\n", | |
"The actual length for each shot is:\n", | |
"\n", | |
"$ {d_k}^* = \\frac{{d\\_{ks}}}{v_s} + \\frac{{d\\_{kn}}}{v_n} $" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The final question is:\n", | |
"\n", | |
"How can we maximum the total scores of different shots under a certain time budget?" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Solution description:\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Run a dynamic programming over the different speedup case.\n", | |
"- $v_s$ range: 1x-6x; $v_n$ range: 1x-1.5x. \n", | |
"- speed up step count: 100.\n", | |
"- actual speedup case [$v_s, v_n$]: [1x, 1x], [1.5x, 1.05x], [2x, 1.1x].... [6x, 1.5x]\n", | |
" \n", | |
" \n", | |
"Maintain a hashmap of dynamic map for that 10 speedup case.\n", | |
"\n", | |
"\n", | |
"For any time budget, we iterate different speedup cases and find the optimal solution." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Change to the evaluation:\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"- how should we evaluate the different recall in the current case?\n", | |
"\n", | |
"- what's the ground truth strategy for different time budgets now?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
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"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 2", | |
"language": "python", | |
"name": "python2" | |
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"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.11" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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