Multi-vehicle detection algorithm through combining Harr and HOG features
Yun Wei,
Qing Tian,
Jianhua Guo,
Wei Huang and
Jinde Cao
Mathematics and Computers in Simulation (MATCOM), 2019, vol. 155, issue C, 130-145
Abstract:
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.
Keywords: Harr features; HOG features; Vehicle detection; Environment perception; Computer vision (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:155:y:2019:i:c:p:130-145
DOI: 10.1016/j.matcom.2017.12.011
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