Blog
The latest news from Google AI
Video Stabilization on YouTube
Friday, May 4, 2012
Posted by Matthias Grundmann, Vivek Kwatra, and Irfan Essa, Research at Google
One thing we have been working on within Research at Google is developing methods for making casual videos look more professional, thereby providing users with a better viewing experience. Professional videos have several characteristics that differentiate them from casually shot videos. For example, in order to tell a story, cinematographers carefully control lighting and exposure and use specialized equipment to plan camera movement.
We have developed a technique that mimics professional camera moves and applies them to videos recorded by hand-held devices. Cinematographers use specialized equipment such as tripods and dollies to plan their camera paths and hold them steady. In contrast, think of a video you shot using a mobile phone camera. How steady was your hand and were you able to anticipate an interesting moment and smoothly pan the camera to capture that moment? To bridge these differences, we propose an algorithm that automatically determines the best camera path and recasts the video as if it were filmed using stabilization equipment. Specifically, we divide the original, shaky camera path into a set of segments, each approximated by either a constant, linear or parabolic motion of the camera. Our optimization finds the best of all possible partitions using a computationally efficient and stable algorithm. For details, check out our
earlier blog post
or read our paper,
Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths
, published in
IEEE CVPR 2011
.
The next time you upload your videos to YouTube, try stabilizing them by going to the
YouTube editor
or directly from the
video manager
by clicking on Edit->Enhancements. For even more convenience, YouTube will automatically detect if your video needs stabilization and offer to do it for you. Many videos on YouTube have already been enhanced using this technology.
More recently, we have been working on a related problem common in videos shot from mobile phones. The camera sensors in these phones contain what is known as an electronic rolling shutter. When taking a picture with a rolling shutter camera, the image is not captured instantaneously. Instead, the camera captures the image one row of pixels at a time, with a small delay when going from one row to the next. Consequently, if the camera moves during capture, it will cause image distortions ranging from shear in the case of low-frequency motions (for instance an image captured from a driving car) to wobbly distortions in the case of high-frequency perturbations (think of a person walking while recording video). These distortions are especially noticeable in videos where the camera shake is independent across frames. For example, take a look at the video below.
Original video with rolling shutter distortions
In our recent paper titled
Calibration-Free Rolling Shutter Removal
, which was awarded the
best paper
at
IEEE ICCP 2012
, we demonstrate a solution to correct these rolling shutter distortions in videos. A significant feature of our approach is that it does not require any knowledge of the camera used to shoot the video. The time delay in capturing two consecutive rows that we mention above is in fact different for every camera and affects the extent of distortions. Having knowledge of this delay parameter can be useful, but difficult to obtain or estimate via calibration. Imagine a video that is already uploaded to YouTube -- it will be challenging to obtain this parameter! Instead, we show that just the visual data in the video has enough information to appropriately describe and compensate for the distortions caused by the camera motion, even in the presence of a rolling shutter. For more information, see the
narrated video description
of our paper.
This technique is already integrated with the
YouTube stabilizer
. Starting today, if you stabilize a video from a mobile phone or other rolling shutter cameras, we will also automatically compensate for rolling shutter distortions. To see our technique in action, check out the video below, obtained after applying rolling shutter compensation and stabilization to the one above.
After stabilization and rolling shutter removal
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
YouTube
Archive
2018
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Google
on
Follow @googleai
Give us feedback in our
Product Forums
.
