2017
DOI: 10.1016/j.patcog.2016.10.012
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High-precision bicycle detection on single side-view image based on the geometric relationship

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Cited by 13 publications
(5 citation statements)
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“…Bicycle detection and classification face similar challenges and accordingly, a blend of methods based on shape detection and wheels recognition has been proposed in [7,8,[25][26][27]. For example, Somasundaram et al [27] examined the performance of different types of texture-based and motion features for the discrimination between bicycles and pedestrians.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Bicycle detection and classification face similar challenges and accordingly, a blend of methods based on shape detection and wheels recognition has been proposed in [7,8,[25][26][27]. For example, Somasundaram et al [27] examined the performance of different types of texture-based and motion features for the discrimination between bicycles and pedestrians.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Tao et al (2021) experimented with different methods for detecting other vehicles in traffic with the camera positioned on the vehicle in order to provide autonomous driving [3]. Lin and Young (2017) proposed a bicycle detector for sideview image based on the observation that a bicycle consists of two wheels in the form of ellipse shapes and a frame in the form of two triangles [4]. Some researchers [5,6] have tried to detect bicycles on the road with only wheel detection.…”
Section: Introductionmentioning
confidence: 99%
“…Specifically, edge arcs that may consist of an ellipse are found by variety of techniques such as the statistical regression method [10], curve segmentation by fitting a set of short line segments on edges [11], connectivity and curvature conditions [12,13]. Subsequently, these arcs are grouped according to the convexity-concavity [9,11,[14][15][16], arc curvature [15], and geometric constraints [17][18][19][20].…”
Section: Perception By 2d Informationmentioning
confidence: 99%
“…, (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) where , and , denotes the th parameters in the vectors and . If ( , ) is less than a threshold ,the ellipses and are concluded to belong to the same ellipse cluster.…”
Section: ) Clustering By Similarities Among Detected Ellipsesmentioning
confidence: 99%
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