A Novel Background Extraction and Updating Algorithm for Vehicle Detection and Tracking

Authors:
Jun Kong;Ying Zheng;Yinghua Lu;Baoxue Zhang
Affiliations:
Key Laboratory for Applied Statistics of MOE, China;Key Laboratory for Applied Statistics of MOE, China;Northeast Normal University, Changchun, Jilin Province, China;Northeast Normal University, Changchun, Jilin Province, China
Venue:
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Year:
2007

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Abstract

This paper proposes a new adaptive background extraction and updating algorithm for vehicle detection and tracking. Gray-level quantification and two attenuation weights are introduced to reduce the impact of environment lighting condition in background extraction method, two discriminant functions are employed to distinguish false moving objects and true moving objects for solving the deadlock problem of background updating. The experimental results show that the proposed method is more robust, accurate and powerful than traditional methods, and is simple to implement and suitable for real-time vehicle detection and tracking.