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Monocular 3D Scene Modeling and Inference: Understanding Multi-Object Traffic Scenes.
- Source :
- Computer Vision - ECCV 2010 (9783642155604); 2010, p467-481, 15p
- Publication Year :
- 2010
-
Abstract
- Scene understanding has (again) become a focus of computer vision research, leveraging advances in detection, context modeling, and tracking. In this paper, we present a novel probabilistic 3D scene model that encompasses multi-class object detection, object tracking, scene labeling, and 3D geometric relations. This integrated 3D model is able to represent complex interactions like inter-object occlusion, physical exclusion between objects, and geometric context. Inference allows to recover 3D scene context and perform 3D multiobject tracking from a mobile observer, for objects of multiple categories, using only monocular video as input. In particular, we show that a joint scene tracklet model for the evidence collected over multiple frames substantially improves performance. The approach is evaluated for two different types of challenging onboard sequences. We first show a substantial improvement to the state-of-the-art in 3D multi-people tracking. Moreover, a similar performance gain is achieved for multi-class 3D tracking of cars and trucks on a new, challenging dataset. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783642155604
- Database :
- Complementary Index
- Journal :
- Computer Vision - ECCV 2010 (9783642155604)
- Publication Type :
- Book
- Accession number :
- 76760665
- Full Text :
- https://doi.org/10.1007/978-3-642-15561-1_34