DocumentCode :
2462258
Title :
What Can Casual Walkers Tell Us About A 3D Scene?
Author :
Rother, Diego ; Patwardhan, Kedar A. ; Sapiro, Guillermo
Author_Institution :
Univ. of Minnesota, Minneapolis
fYear :
2007
fDate :
14-21 Oct. 2007
Firstpage :
1
Lastpage :
8
Abstract :
An approach for incremental learning of a 3D scene from a single static video camera is presented in this paper. In particular, we exploit the presence of casual people walking in the scene to infer relative depth, learn shadows, and segment the critical ground structure. Considering that this type of video data is so ubiquitous, this work provides an important step towards 3D scene analysis from single cameras in readily available ordinary videos and movies. On-line 3D scene learning, as presented here, is very important for applications such as scene analysis, foreground refinement, tracking, biometrics, automated camera collaboration, activity analysis, identification, and real-time computer-graphics applications. The main contributions of this work are then two-fold. First, we use the people in the scene to continuously learn and update the 3D scene parameters using an incremental robust (L1) error minimization. Secondly, models of shadows in the scene are learned using a statistical framework. A symbiotic relationship between the shadow model and the estimated scene geometry is exploited towards incremental mutual improvement. We illustrate the effectiveness of the proposed framework with applications in foreground refinement, automatic segmentation as well as relative depth mapping of the floor/ground, and estimation of 3D trajectories of people in the scene.
Keywords :
feature extraction; learning (artificial intelligence); minimisation; statistical analysis; video surveillance; 3D information extraction; casual walking people; error minimization; incremental learning; online 3D scene learning; scene geometry; shadow model; static video camera; statistical framework; Application software; Biometrics; Cameras; Collaborative work; Computer applications; Image analysis; Layout; Legged locomotion; Motion pictures; Pervasive computing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
Conference_Location :
Rio de Janeiro
ISSN :
1550-5499
Print_ISBN :
978-1-4244-1630-1
Electronic_ISBN :
1550-5499
Type :
conf
DOI :
10.1109/ICCV.2007.4409082
Filename :
4409082
Link To Document :
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