Advanced

In search of a car : Utilizing a 3D Model with Context for Object Detection

Nilsson, Mikael LU and Ardö, Håkan LU (2014) 9th International Conference on Computer Vision Theory and Applications, VISAPP 2014 In VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications 2. p.419-424
Abstract

Automatic video analysis of interactions between road users is desired for city and road planning. A first step of such a system is object localization of road users. In this work, we present a method of detecting a specific car in an intersection from a monocular camera image. A camera calibration and segmentation are utilized as inputs by the method in order to detect a car. Using these inputs, a sampled search space in the ground plane, including rotations, is explored with a 3D model of a car in order to produce output in form of rectangle detections in the ground plane. Evaluation on real recorded data, with ground truth for one car using GPS, indicates that a car can be detected in over 90% of the time with an average error around... (More)

Automatic video analysis of interactions between road users is desired for city and road planning. A first step of such a system is object localization of road users. In this work, we present a method of detecting a specific car in an intersection from a monocular camera image. A camera calibration and segmentation are utilized as inputs by the method in order to detect a car. Using these inputs, a sampled search space in the ground plane, including rotations, is explored with a 3D model of a car in order to produce output in form of rectangle detections in the ground plane. Evaluation on real recorded data, with ground truth for one car using GPS, indicates that a car can be detected in over 90% of the time with an average error around 0.5m. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
3D Model, Camera Calibration, Context, Foreground/Background Segmentation, Ground-plane, Traffic
in
VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
volume
2
pages
6 pages
publisher
SciTePress
conference name
9th International Conference on Computer Vision Theory and Applications, VISAPP 2014
external identifiers
  • scopus:84906916156
ISBN
9789897580048
language
English
LU publication?
yes
id
1c395b3d-f5b7-44e1-bb4a-c54395c6ca93
date added to LUP
2016-04-07 13:32:34
date last changed
2017-01-01 08:22:00
@inproceedings{1c395b3d-f5b7-44e1-bb4a-c54395c6ca93,
  abstract     = {<p>Automatic video analysis of interactions between road users is desired for city and road planning. A first step of such a system is object localization of road users. In this work, we present a method of detecting a specific car in an intersection from a monocular camera image. A camera calibration and segmentation are utilized as inputs by the method in order to detect a car. Using these inputs, a sampled search space in the ground plane, including rotations, is explored with a 3D model of a car in order to produce output in form of rectangle detections in the ground plane. Evaluation on real recorded data, with ground truth for one car using GPS, indicates that a car can be detected in over 90% of the time with an average error around 0.5m. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.</p>},
  author       = {Nilsson, Mikael and Ardö, Håkan},
  booktitle    = {VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications},
  isbn         = {9789897580048},
  keyword      = {3D Model,Camera Calibration,Context,Foreground/Background Segmentation,Ground-plane,Traffic},
  language     = {eng},
  pages        = {419--424},
  publisher    = {SciTePress},
  title        = {In search of a car : Utilizing a 3D Model with Context for Object Detection},
  volume       = {2},
  year         = {2014},
}