Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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International Journal of Computer Vision
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International Journal of Computer Vision
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Video event detection using motion relativity and visual relatedness
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Tracklet descriptors for action modeling and video analysis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Event detection and recognition for semantic annotation of video
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Description of interest regions with center-symmetric local binary patterns
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A Statistical Video Content Recognition Method Using Invariant Features on Object Trajectories
IEEE Transactions on Circuits and Systems for Video Technology
Modeling and representing events in multimedia
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Trajectory signature for action recognition in video
Proceedings of the 20th ACM international conference on Multimedia
E-LAMP: integration of innovative ideas for multimedia event detection
Machine Vision and Applications
Retina enhanced SURF descriptors for spatio-temporal concept detection
Multimedia Tools and Applications
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Video concepts annotation is a challenging problem having strong implication in many applications such as videos search and retrieval. In this work, we focus on the recognition of dynamics events (running, walking) in unconstrained videos. Feature trajectories have been shown to be an efficient video representation [23, 16, 17]. Trajectories are extracted by tracking interest points over several video frames to capture both motion and appearance information. We take advantage of this representation to characterize dynamic concepts in our events recognition system. At the trajectories extraction step, we investigate a new trajectory filtering scheme retaining only trajectories having a significant motions, helping the dynamic events recognition. We also propose two new trajectory-based descriptors. The first descriptor captures the trajectories motion through their first order statistics. The second descriptor studies the trajectories motion derivative to be invariant to uniform camera motion, such as a translation in a traveling scene. We evaluate our proposals on the HOHA dataset, a challenging dataset composed of videos extracted from Hollywood with significant camera motion.