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¡¶½»Í¨ÔËÊ乤³Ìѧ±¨¡·[ISSN:1671-1637/CN:61-1369/U]

Issue:

2015Äê06ÆÚ

Page:

118-126

Research Field:

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Publishing date:

Info

Title:

Vehicle detection and tracking algorithm based on monocular and binocular vision fusion

Author(s):

CAI Ying-feng¹;  WANG Hai²;  CHEN Xiao-bo¹;  JIANG Hao-bin²

1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, Jiangsu, China; 2. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China

Keywords:

vehicle detection;  vehicle tracking;  monocular and binocular vision fusion;  2D deep belief network;  Kalman filter

PACS:

U491.116

DOI:

-

Abstract:

The monocular and binocular vision fusion based vehicle detection and Kalman filter based vehicle tracking algorithm was proposed. The 2D deep belief network based vehicle detector was designed. In road images, the monocular vision was used to generate probably existing area of vehicle that composes vehicle candidate set processed by the binocular vision. The binocular vision was used to further eliminate error detection and obtain vehicle position information. The Kalman filter was used to track detected vehicles in 2D image coordinate system and 3D world coordinate system. Test result shows that the detection rate of the algorithm is 99.0%, the error detection rate is 1.3¡Á10^-4%, and the detection time is 57 ms. So the detection rate is high, the error detection rate is low, and the detection time is short. Compared to the monocular and binocular vision weak fusion algorithm, the monocular vision algorithm and the binocular vision algorithm, the proposed vehicle detection and tracking algorithm has both the advantage of binocular vision with high detection rate and the advantage of monocular vision with short detection time. 1 tab, 12 figs, 17 refs.

References:

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