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weakly-action-localization

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# Weakly Supervised Action Localization by Sparse Temporal Pooling Network

## Overview

This repository contains reproduced code reported in the paper "[Weakly Supervised Action Localization by Sparse Temporal Pooling Network](https://arxiv.org/abs/1712.05080)" by Phuc Nguyen and Ting Liu.
The paper was posted on arXiv in Dec 2017, published as a CVPR 2018 conference paper.

Disclaimer: This is the reproduced code, not an original code of the paper.

## Running the code

see the multi_run.sh

firstly you need to use [Dense_Flow](https://github.com/yjxiong/dense_flow) to extract rgb and optical flow

secondly you need to extract I3D feature using [thumos14-i3d](https://github.com/demianzhang/thumos14-i3d)

finally you can train, test and combine the rgb and flow result

### result

Model          |  0.1  |  0.2  |  0.3  |  0.4  |  0.5
-------------- | :---: | :---: | :---: | :---: | ----
RGB-I3D        | 0.351 | 0.279 | 0.213 | 0.150 | 0.102
Flow-I3D       | 0.408 | 0.340 | 0.269 | 0.205 | 0.144
Two-Stream I3D | - | - | - | - |  -

### please pay attention to the order according to the paper
1. sample 10fps

use opencv cv2.VideoCapture to read the video and keep one frame every three frames[original video in 30fps]

2. keep ratio 340x180 and resize the small edge to 256

after resize frames, then use cv2.VideoWriter to save sampled frames into video

3. extract flow

use dense_flow to extract rgb and flows, please pay attention to the shell default parameter 340x256, you should change it to the video's shape in order to keep the ratio

4. center crop 224x224, each 16 frames are sent to i3d

The result may be a little higher than the result, because I did not obey this order before.

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