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 No Comments »

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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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},
      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}
    }

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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Paper in IROS 2012: “Linguistic Transfer of Human Assembly Tasks to Robots”

October 7th, 2012 Irfan Essa Posted in 0205507, Activity Recognition, IROS/ICRA, Mike Stilman, Robotics No Comments »

Linguistic Transfer of Human Assembly Tasks to Robots

  • N. Dantam, I. Essa, and M. Stilman (2012), “Linguistic Transfer of Human Assembly Tasks to Robots,” in Proceedings of Intelligent Robots and Systems (IROS), 2012. [PDF] [DOI] [BIBTEX]
    @InProceedings{    2012-Dantam-LTHATR,
      author  = {N. Dantam and I. Essa and M. Stilman},
      booktitle  = {Proceedings of Intelligent Robots and Systems
          (IROS)},
      doi    = {10.1109/IROS.2012.6385749},
      pdf    = {http://www.cc.gatech.edu/~ndantam3/papers/dantam2012assembly.pdf},
      title    = {Linguistic Transfer of Human Assembly Tasks to
          Robots},
      year    = {2012}
    }

Abstract

We demonstrate the automatic transfer of an assembly task from human to robot. This work extends efforts showing the utility of linguistic models in verifiable robot control policies by now performing real visual analysis of human demonstrations to automatically extract a policy for the task. This method tokenizes each human demonstration into a sequence of object connection symbols, then transforms the set of sequences from all demonstrations into an automaton, which represents the task-language for assembling a desired object. Finally, we combine this assembly automaton with a kinematic model of a robot arm to reproduce the demonstrated task.

Presented at: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), October 7-12, 2012 Vilamoura, Algarve, Portugal.

 

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Paper in IEEE CVPR 2012: “Detecting Regions of Interest in Dynamic Scenes with Camera Motions”

June 16th, 2012 Irfan Essa Posted in Activity Recognition, Kihwan Kim, Machine Learning, PAMI/ICCV/CVPR/ECCV, Papers, PERSEAS, Visual Surviellance No Comments »

Detecting Regions of Interest in Dynamic Scenes with Camera Motions

  • 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] [DOI] [BLOG] [BIBTEX]
    @InProceedings{    2012-Kim-DRIDSWCM,
      author  = {Kihwan Kim and Dongreyol Lee and Irfan Essa},
      blog    = {http://prof.irfanessa.com/2012/04/09/paper-cvpr2012/},
      booktitle  = {Proceedings of IEEE Conference on Computer Vision
          and Pattern Recognition (CVPR)},
      doi    = {10.1109/CVPR.2012.6247809},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2012-Kim-DRIDSWCM.pdf},
      publisher  = {IEEE Computer Society},
      title    = {Detecting Regions of Interest in Dynamic Scenes
          with Camera Motions},
      url    = {http://www.cc.gatech.edu/cpl/projects/roi/},
      video    = {http://www.youtube.com/watch?v=19BMwDMCSp8},
      year    = {2012}
    }

Abstract

We present a method to detect the regions of interests in moving camera views of dynamic scenes with multiple mov- ing objects. We start by extracting a global motion tendency that reflects the scene context by tracking movements of objects in the scene. We then use Gaussian process regression to represent the extracted motion tendency as a stochastic vector field. The generated stochastic field is robust to noise and can handle a video from an uncalibrated moving camera. We use the stochastic field for predicting important future regions of interest as the scene evolves dynamically.

We evaluate our approach on a variety of videos of team sports and compare the detected regions of interest to the camera motion generated by actual camera operators. Our experimental results demonstrate that our approach is computationally efficient, and provides better prediction than those of previously proposed RBF-based approaches.

Presented at: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012, Providence, RI, June 16-21, 2012

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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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