Monocular vision-based collision avoidance system using shadow detection

Ismail, Loay; Eliyan, Lubna Fayez; Younes, Razan; Ahmed, Rasha

doi:10.1109/ieeegcc.2013.6705845

Cited by 3 publications

(1 citation statement)

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“…However, the grey level threshold separating road and the shadow under a vehicle is not provided in [20]. An alternative solution is proposed in [21] and later used in [22,23,24,25,26,27,28,29], where a coarse approximation of the free driving space is obtained by defining the lowest central region in the image delimited by edges. A normal distribution is assumed for the grey levels of the free driving space, and the shadow under a vehicle is defined as a region with intensity smaller than a threshold m − 3 σ , where m and σ are respectively the mean and standard deviation of the grey levels of road pixels.…”

Section: Related Workmentioning

confidence: 99%

Shadow-Based Vehicle Detection in Urban Traffic

Ibarra-Arenado

Tjahjadi

Pérez-Oria

et al. 2017

Sensors

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Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS.

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