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Multi-vehicle detection algorithm through combining Harr and HOG features

Author

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  • Wei, Yun
  • Tian, Qing
  • Guo, Jianhua
  • Huang, Wei
  • Cao, Jinde

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.

Suggested Citation

  • Wei, Yun & Tian, Qing & Guo, Jianhua & Huang, Wei & Cao, Jinde, 2019. "Multi-vehicle detection algorithm through combining Harr and HOG features," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 130-145.
    • Handle: RePEc:eee:matcom:v:155:y:2019:i:c:p:130-145
    DOI: 10.1016/j.matcom.2017.12.011
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    References listed on IDEAS

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    Cited by:

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    2. Jing, Shaoxue, 2023. "Time-delay Hammerstein system identification using modified cross-correlation method and variable stacking length multi-error algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 288-300.
    3. Junran Lin & Cuimei Yang & Yi Lu & Yuxing Cai & Hanjie Zhan & Zhen Zhang, 2022. "An Improved Soft-YOLOX for Garbage Quantity Identification," Mathematics, MDPI, vol. 10(15), pages 1-12, July.

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