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At ICVSS (International Computer Vision Summer School) 2013, in Calabria, ITALY (July 2013)

July 11th, 2013 Irfan Essa Posted in Computational Photography, Computational Photography and Video, Daniel Castro, Matthias Grundmann, Presentations, S. Hussain Raza, Vivek Kwatra No Comments »

Teaching at the ICVSS 2013, in Calabria, Italy, July 2013 (Programme)

Computational Video: Post-processing Methods for Stabilization, Retargeting and Segmentation

Irfan Essa
(This work in collaboration with
Matthias Grundmann, Daniel Castro, Vivek Kwatra, Mei Han, S. Hussian Raza).

Abstract

We address a variety of challenges for analysis and enhancement of Computational Video. We present novel post-processing methods to bridge the difference between professional and casually shot videos mostly seen on online sites. Our research presents solutions to three well-defined problems: (1) Video stabilization and rolling shutter removal in casually-shot, uncalibrated videos; (2) Content-aware video retargeting; and (3) spatio-temporal video segmentation to enable efficient video annotation. We showcase several real-world applications building on these techniques.

We start by proposing a novel algorithm for video stabilization that generates stabilized videos by employing L1-optimal camera paths to remove undesirable motions. We compute camera paths that are optimally partitioned into con- stant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To achieve this, we propose a linear program- ming framework to minimize the first, second, and third derivatives of the result- ing camera path. Our method allows for video stabilization beyond conventional filtering, that only suppresses high frequency jitter. An additional challenge in videos shot from mobile phones are rolling shutter distortions. Modern CMOS cameras capture the frame one scanline at a time, which results in non-rigid image distortions such as shear and wobble. We propose a solution based on a novel mixture model of homographies parametrized by scanline blocks to correct these rolling shutter distortions. Our method does not rely on a-priori knowl- edge of the readout time nor requires prior camera calibration. Our novel video stabilization and calibration free rolling shutter removal have been deployed on YouTube where they have successfully stabilized millions of videos. We also discuss several extensions to the stabilization algorithm and present technical details behind the widely used YouTube Video Stabilizer.

We address the challenge of changing the aspect ratio of videos, by proposing algorithms that retarget videos to fit the form factor of a given device without stretching or letter-boxing. Our approaches use all of the screens pixels, while striving to deliver as much video-content of the original as possible. First, we introduce a new algorithm that uses discontinuous seam-carving in both space and time for resizing videos. Our algorithm relies on a novel appearance-based temporal coherence formulation that allows for frame-by-frame processing and results in temporally discontinuous seams, as opposed to geometrically smooth and continuous seams. Second, we present a technique, that builds on the above mentioned video stabilization approach. We effectively automate classical pan and scan techniques by smoothly guiding a virtual crop window via saliency constraints.

Finally, we introduce an efficient and scalable technique for spatio-temporal segmentation of long video sequences using a hierarchical graph-based algorithm. We begin by over-segmenting a volumetric video graph into space-time regions grouped by appearance. We then construct a region graph over the ob- tained segmentation and iteratively repeat this process over multiple levels to create a tree of spatio-temporal segmentations. This hierarchical approach gen- erates high quality segmentations, and allows subsequent applications to choose from varying levels of granularity. We demonstrate the use of spatio-temporal segmentation as users interact with the video, enabling efficient annotation of objects within the video.

Part of this talks will will expose attendees to use the Video Stabilizer on YouTube and the video segmentation system at videosegmentation.com. Please find appropriate videos to test the systems.

Part of the work described above was done at Google, where Matthias Grundmann, Vivek Kwatra and Mei Han are, and Professor Essa is working as a Consultant. Part of the work were efforts of research by Matthias Grundmann, Daniel Castro and S. Hussain Raza, as part of their research efforts as students at GA Tech.

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Google I/O 2013: Secrets of Video Stabilization on YouTube

May 28th, 2013 Irfan Essa Posted in Computational Photography and Video, Google, In The News, Matthias Grundmann, Presentations, Vivek Kwatra 1 Comment »

Presentation at Google I/0 2013 by Matthias Grundmann, John Gregg, and Vivek Kwatra on our Video Stabilizer on YouTube

Video stabilization is a key component of YouTubes video enhancement tools and youtube.com/editor. All YouTube uploads are automatically detected for shakiness and suggested stabilization if needed. This talk will describe the technical details behind our fully automatic one-click stabilization technology, including aspects such as camera path optimization, rolling shutter detection and removal, distributed computing for real-time previews, and camera shake detection. More info: http://googleresearch.blogspot.com/2012/05/video-stabilization-on-youtube.html

via Secrets of Video Stabilization on YouTube — Google I/O 2013.

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Presentation (2012): CMU Robotics Institute Seminar

October 19th, 2012 Irfan Essa Posted in Computational Photography and Video, Matthias Grundmann, Presentations, Vivek Kwatra No Comments »

Video Analysis and Enhancement: Video Stabilization and Rolling Shutter Removal on YouTube

Irfan Essa
Georgia Tech
School of Interactive Computing
GVU and RIM @ GT Centers

October 19, 2012, 3:30 PM, NSH 1305

Abstract

In this talk, I will discuss a variety of approaches my group is working on for video analysis and enhancement. In particular, I will describe our approach for a video stabilizer, currently implemented and running on YouTube, and its extensions.

This method generates stabilized videos by employing L1-optimal camera paths to remove undesirable motions [1]. We compute camera paths that are optimally partitioned into constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. We propose a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering that only suppresses high frequency jitter. An additional challenge in videos shot from mobile phones are rolling shutter distortions. Modern CMOS cameras capture the frame one scan-line at a time, which results in non-rigid image distortions such as shear and wobble. I will demonstrate a solution based on a novel mixture model of homographies parametrized by scan-line blocks to correct these rolling shutter distortions [2]. Our method does not rely on a-priori knowledge of the readout time nor requires prior camera calibration. A thorough evaluation based on a user study and direct comparisons to other approaches, demonstrates a general preference for our algorithm.

I will conclude the talk by showcasing a live demo of the stabilizer. This work is in collaboration with Matthias Grundmann and Vivek Kwatra at Google, and appears in following two papers.

Time permitting, I will discuss some other projects we are working on, including video segmentation and retargetting.

[1] Matthias Grundmann, Vivek Kwatra, Irfan Essa, CVPR 2011, www.cc.gatech.edu/cpl/projects/videostabilization

[2] Matthias Grundmann, Vivek Kwatra, Daniel Castro Irfan Essa, ICCP 2012, Best paper, www.cc.gatech.edu/cpl/projects/rollingshutter

Host: Takeo Kanade

via Robotics Institute: Talks and Seminars.

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Paper in ECCV Workshop 2012: “Weakly Supervised Learning of Object Segmentations from Web-Scale Videos”

October 7th, 2012 Irfan Essa Posted in Activity Recognition, Awards, Google, Matthias Grundmann, Multimedia, PAMI/ICCV/CVPR/ECCV, Papers, Vivek Kwatra, WWW No Comments »

Weakly Supervised Learning of Object Segmentations from Web-Scale Videos

  • G. Hartmann, M. Grundmann, J. Hoffman, D. Tsai, V. Kwatra, O. Madani, S. Vijayanarasimhan, I. Essa, J. Rehg, and R. Sukthankar (2012), “Weakly Supervised Learning of Object Segmentations from Web-Scale Videos,” in Proceedings of ECCV 2012 Workshop on Web-scale Vision and Social Media, 2012. [PDF] [DOI] [BIBTEX]
    @inproceedings{2012-Hartmann-WSLOSFWV,
      Author = {Glenn Hartmann and Matthias Grundmann and Judy Hoffman and David Tsai and Vivek Kwatra and Omid Madani and Sudheendra Vijayanarasimhan and Irfan Essa and James Rehg and Rahul Sukthankar},
      Booktitle = {Proceedings of ECCV 2012 Workshop on Web-scale Vision and Social Media},
      Date-Added = {2012-10-23 15:03:18 +0000},
      Date-Modified = {2013-10-22 18:57:10 +0000},
      Doi = {10.1007/978-3-642-33863-2_20},
      Pdf = {http://www.cs.cmu.edu/~rahuls/pub/eccv2012wk-cp-rahuls.pdf},
      Title = {Weakly Supervised Learning of Object Segmentations from Web-Scale Videos},
      Year = {2012},
      Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-642-33863-2_20}}

Abstract

We propose to learn pixel-level segmentations of objects from weakly labeled (tagged) internet videos. Speci cally, given a large collection of raw YouTube content, along with potentially noisy tags, our goal is to automatically generate spatiotemporal masks for each object, such as dog”, without employing any pre-trained object detectors. We formulate this problem as learning weakly supervised classi ers for a set of independent spatio-temporal segments. The object seeds obtained using segment-level classi ers are further re ned using graphcuts to generate high-precision object masks. Our results, obtained by training on a dataset of 20,000 YouTube videos weakly tagged into 15 classes, demonstrate automatic extraction of pixel-level object masks. Evaluated against a ground-truthed subset of 50,000 frames with pixel-level annotations, we con rm that our proposed methods can learn good object masks just by watching YouTube.

Presented at: ECCV 2012 Workshop on Web-scale Vision and Social Media, 2012, October 7-12, 2012, in Florence, ITALY.

Awarded the BEST PAPER AWARD!

 

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Presentation (2012): Distinguished Seminar Series in Computer Science at the Imperial College, London

October 4th, 2012 Irfan Essa Posted in Computational Photography and Video, Matthias Grundmann, Presentations, Vivek Kwatra No Comments »

Video Enhancement and Analysis: From Content Analysis to Video Stabilization for YouTube

Irfan Essa
Georgia Institute of Technology

October 14, 2012 15:00 – 16:00, Huxely Room. South Kensington Campus, Imperial College, London

Abstract

The talk will describe a variety of efforts undertaken on analysis of  video to enhancement and synthesis of video. An overview of the past work on representing and analyzing videos as a stochastic process and use of this in a form of Video Textures will be provided.  Majority of the talk will then focus on the recent effort which resulted in a widely-used video stabilizer currently implemented on YouTube and its extensions. This method generates stabilized videos by employing L1-optimal camera paths to remove undesirable motions. We compute camera paths that are optimally partitioned into constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, we propose a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering that only suppresses high frequency jitter. An additional challenge in videos shot from mobile phones are rolling shutter distortions.  We demonstrate a solution based on a novel mixture model of homographies parametrized by scanline blocks to correct these rolling shutter distortions. Our method does not rely on a-priori knowledge of the readout time nor requires prior camera calibration.  This work is in collaboration with Matthias Grundmann and Vivek Kwatra at Google.

Via Distinguished Seminar Series in Computer Science Irfan Essa – GA Tech.

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AT Texas Instruments to give a Talk on “Video Stabilization and Rolling Shutter Removal on YouTube

August 22nd, 2012 Irfan Essa Posted in Computational Photography and Video, Matthias Grundmann, Presentations, Vivek Kwatra No Comments »

Video Stabilization and Rolling Shutter Removal on YouTube

Abstract

In this talk, I will over a variety of approaches my group is working on for video analysis and enhancement. In particular, I will describe our approach for a video stabilizer (currently implemented on YouTube) and its extensions. This work is in collaboration with Matthias Grundmann and Vivek Kwatra at Google. This method generates stabilized videos by employing L1-optimal camera paths to remove undesirable motions [1]. We compute camera paths that are optimally partitioned into constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, we propose a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering that only suppresses high frequency jitter. An additional challenge in videos shot from mobile phones are rolling shutter distortions. Modern CMOS cameras capture the frame one scanline at a time, which results in non-rigid image distortions such as shear and wobble. I will demonstrate a solution based on a novel mixture model of homographies parametrized by scanline blocks to correct these rolling shutter distortions [2]. Our method does not rely on a-priori knowledge of the readout time nor requires prior camera calibration. A thorough evaluation based on a user study demonstrates a general preference for our algorithm.

I will conclude the talk by showcasing a live demo of the stabilizer and time permitting, I will discuss some other projects we are working on.

[1] Matthias Grundmann, Vivek Kwatra, Irfan Essa, CVPR 2011, www.cc.gatech.edu/cpl/projects/videostabilization

[2] Matthias Grundmann, Vivek Kwatra, Daniel Castro Irfan Essa, ICCP 2012, Best paper, www.cc.gatech.edu/cpl/projects/rollingshutter

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At CVPR 2012, in Providence, RI, June 16 – 21, 2012

June 17th, 2012 Irfan Essa Posted in Activity Recognition, Computational Photography and Video, Kihwan Kim, Matthias Grundmann, PAMI/ICCV/CVPR/ECCV, Presentations, Vivek Kwatra No Comments »

At IEEE CVPR 2012 is in Providence RI, from Jun 16 – 21, 2012.

Busy week ahead meeting good friends and colleagues. Here are some highlights of what my group is involved with.

Paper in Main Conference

  • K. Kim, D. Lee, and I. Essa (2012), “Detecting Regions of Interest in Dynamic Scenes with Camera Motions,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. [PDF] [WEBSITE] [VIDEO] [Poster on Tuesday 6/19/2012]

Demo in Main Conference

  • M. Grundmann, V. Kwatra, D. Castro, and I. Essa (2012), “Calibration-Free Rolling Shutter Removal,” in [WEBSITE] [VIDEO] (Paper in ICCP 2012) [Demo on Monday and Tuesday (6/18-19) at the Google Booth]

Invited Talk in Workshop

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Video Stabilization on YouTube

May 6th, 2012 Irfan Essa Posted in Computational Photography and Video, Google, In The News, Matthias Grundmann, Vivek Kwatra No Comments »

Here is an excerpt from a Google Research Blog on our Video Stabilization on YouTube.  Now even more improved.

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 handheld 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.

Via Video Stabilization on YouTube.

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Paper in IEEE ICCP 2012: “Calibration-Free Rolling Shutter Removal”

April 28th, 2012 Irfan Essa Posted in Computational Photography and Video, Daniel Castro, ICCP, Matthias Grundmann, Vivek Kwatra No Comments »

Calibration-Free Rolling Shutter Removal

  • M. Grundmann, V. Kwatra, D. Castro, and I. Essa (2012), “Calibration-Free Rolling Shutter Removal,” in Proceedings of IEEE Conference on Computational Photography (ICCP), 2012. [PDF] [WEBSITE] [VIDEO] [DOI] [BLOG] [BIBTEX]
    @inproceedings{2012-Grundmann-CRSR,
      Author = {Matthias Grundmann and Vivek Kwatra and Daniel Castro and Irfan Essa},
      Blog = {http://prof.irfanessa.com/2012/04/28/paper-iccp12/},
      Booktitle = {Proceedings of IEEE Conference on Computational Photography (ICCP)},
      Date-Added = {2012-04-09 22:40:38 +0000},
      Date-Modified = {2013-10-22 13:54:12 +0000},
      Doi = {10.1109/ICCPhot.2012.6215213},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2012-Grundmann-CRSR.pdf},
      Publisher = {IEEE Computer Society},
      Title = {Calibration-Free Rolling Shutter Removal},
      Url = {http://www.cc.gatech.edu/cpl/projects/rollingshutter/},
      Video = {http://www.youtube.com/watch?v=_Pr_fpbAok8},
      Year = {2012},
      Bdsk-Url-1 = {http://www.cc.gatech.edu/cpl/projects/rollingshutter/},
      Bdsk-Url-2 = {http://dx.doi.org/10.1109/ICCPhot.2012.6215213}}

Abstract

We present a novel algorithm for efficient removal of rolling shutter distortions in uncalibrated streaming videos. Our proposed method is calibration free as it does not need any knowledge of the camera used, nor does it require calibration using specially recorded calibration sequences. Our algorithm can perform rolling shutter removal under varying focal lengths, as in videos from CMOS cameras equipped with an optical zoom. We evaluate our approach across a broad range of cameras and video sequences demonstrating robustness, scalability, and repeatability. We also conducted a user study, which demonstrates a preference for the output of our algorithm over other state-of-the art methods. Our algorithm is computationally efficient, easy to parallelize, and robust to challenging artifacts introduced by various cameras with differing technologies.

Presented at IEEE International Conference on Computational Photography, Seattle, WA, April 27-29, 2012.

Winner of BEST PAPER AWARD.

 

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Award (2012): Best Computer Vision Paper Award by Google Research

March 22nd, 2012 Irfan Essa Posted in Computational Photography and Video, Matthias Grundmann, Papers, Vivek Kwatra No Comments »

Our following paper was just awarded the Excellent Paper for 2011 in Computer Vision by Google Research.

  • M. Grundmann, V. Kwatra, and I. Essa (2011), “Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [PDF] [WEBSITE] [VIDEO] [DEMO] [DOI] [BLOG] [BIBTEX]
    @inproceedings{2011-Grundmann-AVSWROCP,
      Author = {M. Grundmann and V. Kwatra and I. Essa},
      Blog = {http://prof.irfanessa.com/2011/06/19/videostabilization/},
      Booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      Date-Modified = {2013-10-22 13:55:15 +0000},
      Demo = {http://www.youtube.com/watch?v=0MiY-PNy-GU},
      Doi = {10.1109/CVPR.2011.5995525},
      Month = {June},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2011-Grundmann-AVSWROCP.pdf},
      Publisher = {IEEE Computer Society},
      Title = {Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths},
      Url = {http://www.cc.gatech.edu/cpl/projects/videostabilization/},
      Video = {http://www.youtube.com/watch?v=i5keG1Y810U},
      Year = {2011},
      Bdsk-Url-1 = {http://www.cc.gatech.edu/cpl/projects/videostabilization/},
      Bdsk-Url-2 = {http://dx.doi.org/10.1109/CVPR.2011.5995525}}

Casually shot videos captured by handheld or mobile cameras suffer from significant amount of shake. Existing in-camera stabilization methods dampen high-frequency jitter but do not suppress low-frequency movements and bounces, such as those observed in videos captured by a walking person. On the other hand, most professionally shot videos usually consist of carefully designed camera configurations, using specialized equipment such as tripods or camera dollies, and employ ease-in and ease-out for transitions. Our stabilization technique automatically converts casual shaky footage into more pleasant and professional looking videos by mimicking these cinematographic principles. The original, shaky camera path is divided into a set of segments, each approximated by either constant, linear or parabolic motion, using an algorithm based on robust L1 optimization. The stabilizer has been part of the YouTube Editor youtube.com/editor since March 2011.

via Research Blog.

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