@article{ref1,
title="3D Deformable Model Based Localization and Recognition of Road Vehicles",
journal="IEEE transactions on image processing",
year="2012",
author="Zhang, Z. and Tan, T. and Huang, K. and Wang, Y.",
volume="21",
number="1",
pages="1-13",
abstract="We address the problem of model based object recognition. Our aim is to localize and recognize road vehicles from monocular images or videos in calibrated traffic scenes. A 3D deformable vehicle model with 12 shape parameters is set up as prior information and its pose is determined by 3 parameters which are its position on the ground plane and its orientation about the vertical axis under Ground Plane Constraints (GPC). An efficient local gradient based method is proposed to evaluate the fitness between the projection of vehicle model and image data, which is combined into a novel evolutionary computing framework to estimate the 12 shape parameters and 3 pose parameters by iterative evolution. The recovery of pose parameters achieves vehicle localization while the shape parameters are used for vehicle recognition. Numerous experiments are conducted in this paper to demonstrate the performance of our approach. It is shown that the local gradient based method can accurately and efficiently evaluate the fitness between the projection of vehicle model and image data. The evolutionary computing framework is effective for vehicles of different types and poses, and robust to all kinds of occlusions.<p /> <p>Language: en</p>",
language="en",
issn="1057-7149",
doi="10.1109/TIP.2011.2160954",
url="http://dx.doi.org/10.1109/TIP.2011.2160954"
}