Adaptive Vehicle Shadow Detection Algorithm in Highway

He, Yufeng; Li, Jiatian; Wang, Hua; Pu, Haixia; Li, Rui

doi:10.1109/iscid.2012.211

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…Since the intensity of the shadow area is sensitive to changes of illumination, selecting a fixed threshold to segment the shadow area cannot be applied to various scenes. Therefore, an adaptive threshold algorithm is carried out in [ 75 ], the pixel ratio of each point in the image was first calculated, then two parameters α and β between 0 and 1 were selected and the areas whose pixel ratio between α and β were defined as shadows. Although this method solved the limitation of the fixed threshold methods, the selection of parameters α and β requires constant iteration, which makes it difficult to obtain an optimal solution.…”

Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%