Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern

Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern

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 m...

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Main Authors: Hongyu Hu, Pengfei Tao, Zhenhai Gao, Qingnian Wang, Zhihui Li, Zhaowei Qu
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2014/370685
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spelling doaj-13c617252f0d45a0898c5f874bc9b20a2020-11-24T22:00:50ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472014-01-01201410.1155/2014/370685370685Vision-Based Bicycle Detection Using Multiscale Block Local Binary PatternHongyu Hu0Pengfei Tao1Zhenhai Gao2Qingnian Wang3Zhihui Li4Zhaowei Qu5State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaCollege of Transportation, Jilin University, Changchun 130022, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaCollege of Transportation, Jilin University, Changchun 130022, ChinaCollege of Transportation, Jilin University, Changchun 130022, ChinaBicycle 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.http://dx.doi.org/10.1155/2014/370685
collection DOAJ
language English
format Article
sources DOAJ
author Hongyu Hu
Pengfei Tao
Zhenhai Gao
Qingnian Wang
Zhihui Li
Zhaowei Qu
spellingShingle Hongyu Hu
Pengfei Tao
Zhenhai Gao
Qingnian Wang
Zhihui Li
Zhaowei Qu
Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern
Mathematical Problems in Engineering
author_facet Hongyu Hu
Pengfei Tao
Zhenhai Gao
Qingnian Wang
Zhihui Li
Zhaowei Qu
author_sort Hongyu Hu
title Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern
title_short Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern
title_full Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern
title_fullStr Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern
title_full_unstemmed Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern
title_sort vision-based bicycle detection using multiscale block local binary pattern
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2014-01-01
description 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.
url http://dx.doi.org/10.1155/2014/370685
work_keys_str_mv AT hongyuhu visionbasedbicycledetectionusingmultiscaleblocklocalbinarypattern
AT pengfeitao visionbasedbicycledetectionusingmultiscaleblocklocalbinarypattern
AT zhenhaigao visionbasedbicycledetectionusingmultiscaleblocklocalbinarypattern
AT qingnianwang visionbasedbicycledetectionusingmultiscaleblocklocalbinarypattern
AT zhihuili visionbasedbicycledetectionusingmultiscaleblocklocalbinarypattern
AT zhaoweiqu visionbasedbicycledetectionusingmultiscaleblocklocalbinarypattern
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