Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 11, Issue 2, Pages 423-440Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2010.2047500
Keywords
Clustering; generic vehicle models; traffic surveillance; video-based 3-D modeling; 3-D vehicle modeling
Categories
Funding
- National Science Foundation [0551741, 0905671]
- Direct For Computer & Info Scie & Enginr [0551741] Funding Source: National Science Foundation
- Division Of Computer and Network Systems [0551741] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [905671] Funding Source: National Science Foundation
Ask authors/readers for more resources
In this paper, we present a new generic model-based approach for building 3-D models of vehicles from color video from a single uncalibrated traffic-surveillance camera. We propose a novel directional template method that uses trigonometric relations of the 2-D features and geometric relations of a single 3-D generic vehicle model to map 2-D features to 3-D in the face of projection and foreshortening effects. We use novel hierarchical structural similarity measures to evaluate these single-frame-based 3-D estimates with respect to the generic vehicle model. Using these similarities, we adopt a weighted clustering technique to build a 3-D model of the vehicle for the current frame. The 3-D features are then adaptively clustered again over the frame sequence to generate an incremental 3-D model of the vehicle. Results are shown for several simulated and real traffic videos in an uncontrolled setup. Finally, the results are evaluated by the same structural performance measure, underscoring the usefulness of incremental learning. The performance of the proposed method for several types of vehicles in two considerably different traffic spots is very promising to encourage its applicability in 3-D reconstruction of other rigid objects in video.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available