Open Access

ARTICLE

3D Bounding Box Proposal for on-Street Parking Space Status Sensing in Real World Conditions

Yaocheng Zheng¹, Weiwei Zhang^1,*, Xuncheng Wu¹, Bo Zhao¹

1 College of Mechanical Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China.

* Corresponding Author: Weiwei Zhang. Email: .

Computer Modeling in Engineering & Sciences 2019, 119(3), 559-576. https://doi.org/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 maps with five different 3D anchors, which generated by clustering diverse scales of ground truth box according to different vehicle templates in the source data set. Subsequently, vehicle distribution map is constructed jointly from the coordinates of vehicle box and artificially segmented parking spaces, where the normative degree of parked vehicle is calculated by computing the intersection over union between vehicle’s box and parking space edge. In space status inference, to further eliminate mutual vehicle interference, three adjacent spaces are combined into one unit and then a multinomial logistic regression model is trained to refine the status of the unit. Experiments on KITTI benchmark and Shanghai road show that the proposed method outperforms most monocular approaches in 3D box regression and achieves satisfactory accuracy in space status inference.

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Keywords

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