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摘要:
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.
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篇名 Boosted Vehicle Detection Using Local and Global Features
来源期刊 信号与信息处理(英文) 学科 工学
关键词 Vehicle Detection ADABOOST PROBABILISTIC Decision-Based Neural Network (PDBNN) GAUSSIAN MIXTURE Model (GMM)
年,卷(期) 2013,(3) 所属期刊栏目
研究方向 页码范围 243-252
页数 10页 分类号 TP39
字数 语种
DOI
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研究主题发展历程
节点文献
Vehicle
Detection
ADABOOST
PROBABILISTIC
Decision-Based
Neural
Network
(PDBNN)
GAUSSIAN
MIXTURE
Model
(GMM)
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
信号与信息处理(英文)
季刊
2159-4465
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
301
总下载数(次)
0
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