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

Content Provider	Springer Nature Link
Author	Wang, Ke Huang, Zhi Zhong, Zhihua
Copyright Year	2014
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.
Starting Page	1169
Ending Page	1177
Page Count	9
File Format	PDF
ISSN	10009345
Journal	Chinese Journal of Mechanical Engineering
Volume Number	27
Issue Number	6
e-ISSN	21928258
Language	English
Publisher	Chinese Mechanical Engineering Society
Publisher Date	2014-08-19
Publisher Place	Beijing
Access Restriction	Subscribed
Subject Keyword	simultaneous detection and tracking pavement segmentation layered machine learning particle filter Mechanical Engineering Theoretical and Applied Mechanics Manufacturing, Machines, Tools Engineering Thermodynamics, Heat and Mass Transfer Power Electronics, Electrical Machines and Networks Electronics and Microelectronics, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Mechanical Engineering

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