Visualization and Intelligent Systems Laboratory
VISLab

 

 

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VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

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ENTM
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Keio University

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IEEE Biometrics Workshop 2014
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Worshop on DVSN 2009
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Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Learning-Based 3D Vehicle Recognition Using Dynamic Sensor Fusion

Presented by: Nirmalya Ghosh

Abstract:

Vehicle recognition has applications in automated traffic congestion control, non-stop highroad-toll-centers and sophisticated security systems. Conventional 2D vehicle-recognition with input from a single-sensor lacks the robustness and applicability of the approach to full diurnal cycle. Since it cannot adapt to environmental changes. In this work, a 3D vehicle recognition approach is proposed, which integrates the supervisory statistical learning and dynamic sensor fusion. Cooperation of infrared and color-video information is proposed to be tuned with the changing environmental conditions to make the approach applicable throughout the diurnal cycle. Incremental model building will be done from the spatio-temporal information of the color-video and IR frame-sequences for geometry / statistics-based 3D model of the vehicle in visible and infrared spectral regions. Physics based visible and infrared models will be developed considering the vehicle shape and thermodynamics of the vehicle-surfaces. Environmental changes will be accounted by the learning methods to tune the cooperative co-evolutionary sensor-fusion strategy to perform well throughout the day. The Bayesian-type classifier will consider optimality of feature selection and map these features to index the 3D database model developed for vehicles of different makes and shapes.