期刊
MATHEMATICS AND COMPUTERS IN SIMULATION
卷 155, 期 -, 页码 130-145出版社
ELSEVIER
DOI: 10.1016/j.matcom.2017.12.011
关键词
Harr features; HOG features; Vehicle detection; Environment perception; Computer vision
类别
资金
- National Natural Science Foundation of China [61573106]
In order to achieve a better performance of detection and tracking of multi-vehicle targets in complex urban environment, we propose a two-step detection algorithm based on combining the features of Harr and Histogram of Oriented Gradients (HOG). This algorithm makes full use of HOG characteristic advantages for target vehicles, i.e., the good descriptive ability of HOG feature, and the prospect region of interest (ROI) can be extracted using Harr features. Moreover, the extracted HOG features from the ROI target area can be selected through applying the cascade structured AdaBoost classifier features and target area classification. Precise target can be further extracted by using support vector machine (SVM). Experimental results using video collected from real world scenarios are provided, showing that the proposed method possesses higher detecting accuracy and time efficiency than the conventional ones, and it can detect and track the multi-vehicle targets successfully in complex urban environment. (C) 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
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