A Robust Vehicle Tracking Approach Using Mean Shift Procedure

Shen, Huan; Li, Shun Ming; Mao, Jian Guo; Bo, Fang Chao; Li, Fang Pei; Zou, Hua

doi:10.1109/ias.2009.127

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…In his work, sample points with zero probability density gradients were found and served as the pattern points of characteristics of spatial clustering model. Literature [13] combined the orientation histogram and HIS model of the object area and used Mean-Shift to search the potential object in the next frame. Literature [14] extracted the effective feature by SIFT algorithm, and used the feature point of SIFT as the reference point of Mean-Shift to calculate the center of the object area.…”

Section: Vehicle Tracking For Intelligent Vehiclementioning

confidence: 99%

A Review of Vision-Based Vehicle Detection and Tracking Techniques for Intelligent Vehicle

Li¹,

Tian²,

Zhang³

et al. 2015

Advances in Intelligent Systems Research

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Abstract. Vision-based vehicle detection and tracking techniques is of great importance to reduce vehicle collision accidents and increase the driving safety on road. This paper presents a comprehensive review of latest techniques for vehicle detection and tracking. In hypothesis generation of vehicles, motion-based, knowledge-based and stereo-vision based methods are introduced. Hypothesis verification includes template-based, appearance-based and multi-features fusion methods. In addition, three main algorithms are introduced in vehicle tracking. Finally, existing problems and future research directions of this field are summarized.

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Section: Vehicle Tracking For Intelligent Vehiclementioning

confidence: 99%