vijayvee / video-captioning

This repository contains the code for a video captioning system inspired by Sequence to Sequence -- Video to Text. This system takes as input a video and generates a caption in English describing the video.

Automated Video Captioning using S2VT

Introduction

This repository contains my implementation of a video captioning system. This system takes as input a video and generates a caption describing the event in the video.

I took inspiration from Sequence to Sequence -- Video to Text, a video captioning work proposed by researchers at the University of Texas, Austin.

Requirements

For running my code and reproducing the results, the following packages need to be installed first. I have used Python 2.7 for the whole of this project.

Packages:

• TensorFlow
• Caffe
• NumPy
• cv2
• imageio
• scikit-image

S2VT - Architecture and working

Attached below is the architecture diagram of S2VT as given in their paper.

The working of the system while generating a caption for a given video is represented below diagrammatically.

Running instructions

1. Install all the packages mentioned in the 'Requirements' section for the smooth running of this project.
2. Using Vid2Url_Full.txt, download the dataset clips from Youtube and store in <YOUTUBE_CLIPS_DIR>.
   • Example to use Vid2Url - {'vid1547': 'm1NR0uNNs5Y_104_110'}
   • YouTube video identifier - m1NR0uNNs5Y
   • Start time - 104 seconds, End time - 110 seconds
   • Download frames between 104 seconds and 110 seconds in https://www.youtube.com/watch?v=m1NR0uNNs5Y
   • Relevant frames for video id 'vid1547' have been downloaded
3. Pass downloaded video paths and batch size (depending on hardware constraints) to extract_feats() in Extract_Feats.py to extract VGG16 features for the downloaded video clips and store in <VIDEO_DIR>.
4. Change paths in lines 13 to 16 in utils.py to point to directories in your workspace.
5. Run training_vidcap.py with the number of epochs as a command line argument. eg. python training_vidcap.py 10
6. Pass saved checkpoint files from Step 5 to test_videocap.py to run trained model on the validation set.

Sample results

Attached below are a few screenshots from caption generation for videos from the validation set.

Dataset

Even though S2VT was trained on MSVD, M-VAD and MPII-MD, I have trained my system only on MSVD, which can be downloaded here.

Demo

A demo of my system can be found here

Acknowledgements

• Sequence to Sequence -- Video to Text - Subhasini Venugopalan et al.