am-i-done

This repository contains code for reproduction and subsequent analysis of the paper titled "Am I done? Predicting action progress in videos". This reproduction is carried out as part of a course project for the course Deep Learning.

The full description of the reproduction and our findings can be found at Predicting action progress in videos - paper reproduction

Steps to reproduce this work

Clone our fork of the original faster R-CNN repository

git clone https://github.com/gsotirchos/realtime-action-detection

Download the pre-trained faster R-CNN model (rgb-ssd300_ucf24_120000.pth) from the authors' google drive, or with gdown
```
gdown https://drive.google.com/uc?id=1IyqjUQofRyYrAQ-Uz7MPsSqVBlBe-Zk7
```
Download the UCF24 dataset used in the faster R-CNN paper from the same google drive link, or with gdown
```
gdown https://drive.google.com/uc?id=1o2l6nYhd-0DDXGP-IPReBP4y1ffVmGSE
```
Place both the pre-trained model called rgb-ssd300_ucf24_120000.pth and the downloaded .tar.gz file in the realtime-action-detection directory, and extract the dataset tarball
```
mv rgb-ssd300_ucf24_120000.pth realtime-action-detection/
tar -xvf ucf24.tar.gz
mv ucf24 realtime-action-detection/
```
Outside the realtime-action-detection directory, clone this repo:
```
git clone https://github.com/anishhdiwan/am-i-done
```
Run main.py inside the am-i-done directory to train the LSTM model from the reproduced paper OR run load_faster_r_cnn.py inside the realtime-action-detection directory to get frame-wise faster R-CNN detections
```
cd am-i-done
python3 main.py
# OR
cd realtime-action-detection
python3 load_faster_r_cnn.py
```

Original work on Faster R-CNN

Singh G, Saha S, Sapienza M, Torr PH, Cuzzolin F. Online real-time multiple spatiotemporal action localisation and prediction. In Proceedings of the IEEE International Conference on Computer Vision 2017 (pp. 3637-3646).

anishhdiwan/am-i-done

am-i-done

Steps to reproduce this work

Original work on Faster R-CNN