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Simultaneous multi-vehicle detection and tracking framework with pavement constraints based on machine learning and particle filter algorithm

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Abstract

Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.

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Authors and Affiliations

  1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China

    Ke Wang, Zhi Huang & Zhihua Zhong

  2. College of Automotive Engineering, Chongqing University, Chongqing, 400044, China

    Ke Wang

Authors
  1. Ke Wang
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  2. Zhi Huang
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  3. Zhihua Zhong
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Corresponding author

Correspondence to Zhi Huang.

Additional information

Supported by Open Research Fund of State Key Laboratory of Advanced Technology for Vehicle Body Design & Manufacture of China (Grant No. 61075002) and Hunan Provincial Natural Science Foundation of China (Grant No. 13JJ4033)

WANG Ke, born in 1984, is currently a lecturer at College of Automobile Engineering, Chongqing University, China. He received his PhD degree in mechanical engineering from Hunan University, China, in 2013. His research interests include active safety, intelligent vehicle and computer version.

HUANG Zhi, born in 1977, is currently an associate professor at State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, China. He received his PhD degree from Hunan University, China, in 2006. His research interests include automobile active safety technology, intelligent vehicle and. anti-lock braking system.

ZHONG Zhihua, born in 1962, an expert in automotive engineering, was elected as a member of the Chinese Academy of Engineering in 2005. He is currently a professor and a PhD candidate supervisor at State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, China. His research focuses on the vehicle safety technology, shaping technology of the auto body.

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Wang, K., Huang, Z. & Zhong, Z. Simultaneous multi-vehicle detection and tracking framework with pavement constraints based on machine learning and particle filter algorithm. Chin. J. Mech. Eng. 27, 1169–1177 (2014). https://doi.org/10.3901/CJME.2014.0707.118

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  • DOI: https://doi.org/10.3901/CJME.2014.0707.118

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