Share Email Print

Proceedings Paper

A system for vehicle recognition in video based on SIFT features, mixture models, and support vector machines
Author(s): Abhikesh Nag; David J. Miller; Andrew P. Brown; Kevin J. Sullivan
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We present a system for scale and affine invariant recognition of vehicular objects in video sequences. We use local descriptors (SIFT keypoints) from image frames to model the object. These features are claimed in the literature to be highly distinctive and invariant to rotation, scale, and affine transformations. However, since the SIFT keypoints that are extracted from an object are instance-specific (variable), they form a dynamic feature space. This presents certain challenges for classification techniques, which generally require use of the same set of features for every instance of an object to be classified. To resolve this difficulty, we associate the extracted keypoints to the components (representative keypoints) in a mixture model for each target class. While the extracted keypoints are variable, the mixture components are fixed. The mixture models the keypoint features, as well as the location and scale at which each keypoint was detected in the frame. Keypoint to component association is achieved via a switching optimization procedure that locally maximizes the joint likelihood of keypoints and their locations and scales with the latter based on an affine transformation. To each mixture component from a class, we link a (first layer) support vector machine (SVM) classifier which votes for or against the hypothesis that the keypoint associated to the component belongs to the model's target class. A second layer SVM pools the votes from the ensemble of SVM classifiers in the first layer and gives the final class decision. We show promising results of experiments for video sequences from the VIVID database.

Paper Details

Date Published: 30 April 2007
PDF: 10 pages
Proc. SPIE 6560, Intelligent Computing: Theory and Applications V, 65600G (30 April 2007); doi: 10.1117/12.723746
Show Author Affiliations
Abhikesh Nag, The Pennsylvania State Univ. (United States)
David J. Miller, The Pennsylvania State Univ. (United States)
Andrew P. Brown, Toyon Research Corp. (United States)
Kevin J. Sullivan, Toyon Research Corp. (United States)

Published in SPIE Proceedings Vol. 6560:
Intelligent Computing: Theory and Applications V
Kevin L. Priddy; Emre Ertin, Editor(s)

© SPIE. Terms of Use
Back to Top