Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Color-Based Video Stabilization for Real-Time On-Board Object Detection on High-Speed Trains
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Action Recognition in aWearable Assistance System
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Matching actions in presence of camera motion
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse B-spline polynomial descriptors for human activity recognition
Image and Vision Computing
Global motion estimation: feature-based, featureless, or both ?!
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Shape-Motion based athlete tracking for multilevel action recognition
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Action recognition in broadcast tennis video using optical flow and support vector machine
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
Digital video stabilization through curve warping techniques
IEEE Transactions on Consumer Electronics
A fast parametric motion estimation algorithm with illumination and lens distortion correction
IEEE Transactions on Image Processing
Efficient block motion estimation using integral projections
IEEE Transactions on Circuits and Systems for Video Technology
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Video understanding has attracted significant research attention in recent years, motivated by interest in video surveillance, rich media retrieval and vision-based gesture interfaces. Typical methods focus on analyzing both the appearance and motion of objects in video. However, the apparent motion induced by a moving camera can dominate the observed motion, requiring sophisticated methods for compensating for camera motion without a priori knowledge of scene characteristics. This paper introduces two new methods for global motion compensation that are both significantly faster and more accurate than state of the art approaches. The first employs RANSAC to robustly estimate global scene motion even when the scene contains significant object motion. Unlike typical RANSAC-based motion estimation work, we apply RANSAC not to the motion of tracked features but rather to a number of segments of image projections. The key insight of the second method involves reliably classifying salient points into foreground and background, based upon the entropy of a motion inconsistency measure. Extensive experiments on established datasets demonstrate that the second approach is able to remove camera-based observed motion almost completely while still preserving foreground motion.