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JRM Vol.18 No.6 pp. 744-750
doi: 10.20965/jrm.2006.p0744
(2006)

Paper:

An Object Detection Method Based on Independent Local Features

Ryouta Nakano, Kazuhiro Hotta, and Haruhisa Takahashi

The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan

Received:
March 29, 2006
Accepted:
August 28, 2006
Published:
December 20, 2006
Keywords:
object detection, ICA, SVM, HLAC features
Abstract

This paper presents an object detection method using independent local feature extractor. Since objects are composed of a combination of characteristic parts, a good object detector could be developed if local parts specialized for a detection target are derived automatically from training samples. To do this, we use Independent Component Analysis (ICA) which decomposes a signal into independent elementary signals. We then used the basis vectors derived by ICA as independent local feature extractors specialized for a detection target. These feature extractors are applied to a candidate area, and their outputs are used in classification. However, the number of dimension of extracted independent local features is very high. To reduce the extracted independent local features efficiently, we use Higher-order Local AutoCorrelation (HLAC) features to extract the information that relates neighboring features. This may be more effective for object detection than simple independent local features. To classify detection targets and non-targets, we use a Support Vector Machine (SVM). The proposed method is applied to a car detection problem. Superior performance is obtained by comparison with Principal Component Analysis (PCA).

Cite this article as:
Ryouta Nakano, Kazuhiro Hotta, and Haruhisa Takahashi, “An Object Detection Method Based on Independent Local Features,” J. Robot. Mechatron., Vol.18, No.6, pp. 744-750, 2006.
Data files:
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