CVPR
At CVPR 2012, in Providence, RI, June 16 – 21, 2012
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 […]
Paper in IEEE CVPR 2012: "Detecting Regions of Interest in Dynamic Scenes with Camera Motions"
Detecting Regions of Interest in Dynamic Scenes with Camera Motions 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 […]
Presentation at CVPR 2012 workshop on Large Scale Video Search and Mining "Extracting Content and Context from Video."
Extracting Content and Context from Video. (Presentation at CVPR 2012 workshop on Large Scale Video Search and Mining 2012, June 21, 2012)Irfan EssaGEORGIA Tech Abstract In this talk, I will describe various efforts aimed at extracting context and content from video. I will highlight some of our recent work in extracting spatio-temporal features and the […]
DEMO (2011): Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths – from Google Research Blog
via Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths – Google Research Blog. Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths Posted by Matthias Grundmann, Vivek Kwatra, and Irfan Essa, Earlier this year, we announced the launch of new features on the YouTube Video Editor, including stabilization for shaky videos, with the ability to preview them in […]
Paper (2011) in IEEE CVPR: “Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths”
Paper / Citation Abstract We present a novel algorithm for automatically applying constrainable, L1-optimal camera paths to generate stabilized videos by removing undesired motions. Our goal is to compute camera paths that are composed of constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, our algorithm is based […]
Presentation (2011) at IBPRIA 2011: “Spatio-Temporal Video Analysis and Visual Activity Recognition”
“Spatio-Temporal Video Analysis and Visual Activity Recognition” at the Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA) 2011 Conference in Las Palmas de Gran Canaria. Spain. June 8-10. Abstract My research group is focused on various approaches for (a) low-level video analysis and synthesis and (b) recognizing activities in videos. In this talk, I will concentrate […]
Paper in CVPR (2010): "Motion Field to Predict Play Evolution in Dynamic Sport Scenes
Kihwan Kim, Matthias Grundmann, Ariel Shamir, Iain Matthews, Jessica Hodgins, Irfan Essa (2010) “Motion Field to Predict Play Evolution in Dynamic Sport Scenes” in Proceedings of IEEE Computer Vision and Pattern Recognition Conference (CVPR), San Francisco, CA, USA, June 2010 [PDF][Website][DOI][Video (Youtube)]. Abstract Videos of multi-player team sports provide a challenging domain for dynamic scene analysis. Player actions […]
Paper in CVPR (2010): "Discontinuous Seam-Carving for Video Retargeting"
Discontinuous Seam-Carving for Video Retargeting Matthias Grundmann, Vivek Kwatra, Mei Han, Irfan Essa (2010) “Discontinuous Seam-Carving for Video Retargeting” in Proceedings of IEEE Computer Vision and Pattern Recognition Conference (CVPR), San Francisco, CA, USA, June 2010 [PDF][Website][DOI][Video (Youtube)]. [bibtex file=IrfanEssaWS.bib format=ieeeyr template=irfan-bibtex key=2010-Grundmann-DSVR] Abstract We introduce a new algorithm for video retargeting that uses discontinuous seam-carving in both […]
Paper in CVPR (2010): "Efficient Hierarchical Graph-Based Video Segmentation
Matthias Grundmann, Vivek Kwatra, Mei Han, Irfan Essa (2010) “Efficient Hierarchical Graph-Based Video Segmentation” in Proceedings of IEEE Computer Vision and Pattern Recognition Conference (CVPR), San Francisco, CA, USA, June 2010 [PDF][Website][DOI][Video (Youtube)]. Abstract We present an efficient and scalable technique for spatiotemporal segmentation of long video sequences using a hierarchical graph-based algorithm. We begin by over-segmenting a […]