Yaocheng Zheng1, Weiwei Zhang1,*, Xuncheng Wu1, Bo Zhao1
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 559-576, 2019, DOI:10.32604/cmes.2019.05684
Abstract Vision-based technologies have been extensively applied for on-street parking space sensing, aiming at providing timely and accurate information for drivers and improving daily travel convenience. However, it faces great challenges as a partial visualization regularly occurs owing to occlusion from static or dynamic objects or a limited perspective of camera. This paper presents an imagery-based framework to infer parking space status by generating 3D bounding box of the vehicle. A specially designed convolutional neural network based on ResNet and feature pyramid network is proposed to overcome challenges from partial visualization and occlusion. It predicts 3D box candidates on multi-scale feature… More >
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