Boosted Vehicle Detection Using Local and Global Features

Chin-Teng Lin², Sheng-Chih Hsu², Ja-Fan Lee¹, Chien-Ting Yang²

¹Institue of Biomedical Engineering, College of Computer Science of the National Chaio-Tung University, Hsinchu.
²Institue of Electrical and Control Engineering, College of Electrical and Computer Engineering of the National Chiao-Tung University, Hsinchu.

DOI: 10.4236/jsip.2013.43032                

Abstract

This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.

Keywords

Vehicle Detection; AdaBoost; Probabilistic Decision-Based Neural Network (PDBNN); Gaussian Mixture Model (GMM)

Conflicts of Interest

The authors declare no conflicts of interest.

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