A hybrid Hough-Hausdorff method for recognizing bicycles in natural scenes

Dukesherer, J.H.; Smith, C.E.

doi:10.1109/icsmc.2001.972932

Cited by 4 publications

(1 citation statement)

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“…They localized the wheels by searching for ellipses using the generalized Hough-Transform in the edge map to recognize bicycles. In a similar way, in Dukesherer and Smith's method [11], Hough-Transform is utilized to locate wheel regions of bicycles, and then the Hausdorff distance is used for matching the candidates with simple bicycle templates. A bicycle is recognized as two arcs of a circle separated by an approximately known distance.…”

Section: Introductionmentioning

confidence: 99%

Vision‐Based Bicycle Detection Using Multiscale Block Local Binary Pattern

Tao

Gao

et al. 2014

Mathematical Problems in Engineering

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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.

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Section: Introductionmentioning

confidence: 99%