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RECHNER
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A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint
Exploring the Space of a Human Action
A General Framework for Temporal Video Scene Segmentation
Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras
A Supervised Learning Framework for Generic Object Detection in Images
TemporalBoost for Event Recognition
Object Tracking across Multiple Independently Moving Aerial Cameras
Motion Layer Extraction in the Presence of Occlusion Using Graph Cut
Integrating and Employing Multiple Levels of Zoom for Activity Recognition
View-invariant Alignment and Matching of Video Sequences
Two-Frame Wide Baseline Matching
Tracking across Multiple Cameras with Disjoint Views
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
Scene Detection in Hollywood Movies and TV Shows
Human Tracking in Multiple Cameras
A Framework for Segmentation of Talk and Game Shows
Object Based Segmentation of Video Using Color, Motion and Spatial Information
View-Invariance in Action Recognition
View-Invariant Representation and Learning of Human Action
Using Algebraic Functions of Views for Indexing-Based Object Recognition