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Mathematical Problems in Engineering
Volume 2014, Article ID 370685, 7 pages
Research Article

Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern

1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
2College of Transportation, Jilin University, Changchun 130022, China

Received 24 June 2014; Accepted 21 September 2014; Published 1 October 2014

Academic Editor: Wuhong Wang

Copyright © 2014 Hongyu Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Bicycle traffic has heavy proportion among all travel modes in some developing countries, which is crucial for urban traffic control and management as well as facility design. This paper proposes a real-time multiple bicycle detection algorithm based on video. At first, an effective feature called multiscale block local binary pattern (MBLBP) is extracted for representing the moving object, which is a well-classified feature to distinguish between bicycles and nonbicycles; then, a cascaded bicycle classifier trained by AdaBoost algorithm is proposed, which has a good computation efficiency. Finally, the method is tested with video sequence captured from the real-world traffic scenario. The bicycles in the test scenario are successfully detected.