Vacant Parking Space Detection Based on Plane-Based Bayesian Hierarchical Framework

Huang, Ching-Chun; Tai, Yu-Shu; Wang, Sheng-Jyh

doi:10.1109/tcsvt.2013.2254961

Cited by 56 publications

(34 citation statements)

References 25 publications

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“…The authors performed experiments on PKLot and their dataset (now publicly available). A Bayesian framework was designed to detect vacant parking spaces in [285]. The proposed plane-based method adopts a structural 3D parking space model, which has abundant planar surfaces.…”

Section: ) Smart Parkingmentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

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Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and lowlatency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driverless vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed. INDEX TERMS Machine learning, VANET, cognitive radio, autonomous vehicles, smart transportation system. performance of CR-VANET [15]. Security enhancement is one of the major issues in CR-VANET. Here, a vehicle can pretend to be a PU and propagate false information to obtain spectrum access selfishly. ML can be used to detect such actions and enhance security [16], [17]. ML also provides an optimum route to CR-VANET users to avoid traffic jams and road accidents. ML can also play a vital role in the best infotainment experience in CR-VANET. It can be used for appropriate scheduling, selecting the best channel, and prioritizing messages. CR and ML can play a major role in the next-generation driverless car system. The role of CR in the next-generation transportation system has been presented in previous discussions. This survey shows how ML can be applied to reduce road accidents and traffic congestion. CR can be used to accommodate the spectrum required to support massive data communication among automated driverless vehicles and networks. ML can be an integral part of this driverless or automated vehicle system. Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning. This paper presents the dynamic usages of ML in CR-VANET elaborately. Several of the benefits of CR in VANETs and ML in VANETs and CR-VANETs are presented in Table 1. B. CONTRIBUTIONS OF THIS SURVEY ARTICLE Many survey articles describe CR, VANET, ML, and CR-VANETs individually or describe a few aspects of their amalgamation. To the best of our knowledge, surveys that cover the usage of ML in CR-VANET...

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Section: ) Smart Parkingmentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

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“…lighting variations and occlusion. Huang et al [Huang, Tai and Wang (2013)] proposed a plane-based 3D scene model composed of plentiful planar surfaces, which contribute to handling inter-object occlusion and perspective distortion. By means of scene layer, label layer, and observation layer, the plane-based Bayesian hierarchical framework is presented to address challenges of status inference and performance improvement.…”

Section: Related Work 21 Parking Space Sensingmentioning

confidence: 99%

“…Based on a specifically designed deep Convolutional Neural Network (CNN), they integrated the entire system on a smart camera and obtained real-time parking space information subsequently. Similar to the method in Huang et al [Huang, Tai and Wang (2013)], A Multilayer Inference Framework based on Bayesian network for vacant parking lot detection was presented in Huang et al [Huang and VU (2017)]. The framework consists of four component layers: an image layer, a patch layer, a space layer, and a lot layer.…”

Section: Related Work 21 Parking Space Sensingmentioning

confidence: 99%

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

Zheng¹,

Zhang²,

Wu³

et al. 2019

Computer Modeling in Engineering &Amp; Sciences

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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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“…Desirable target positions are designated based on the infrastructure and the vehicle receives the parking information through vehicle-infrastructure communication [3][4][5][6][7][8]. Obviously, the infrastructure-based methods have an advantage of managing all parking-slots; however, they may not be applicable in a short time due to the requirement of additional hardware installation on current park-sites and vehicles.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach

Zhang

Huang

et al. 2018

Symmetry

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Recent years have witnessed a growing interest in developing automatic parking systems in the field of intelligent vehicles. However, how to effectively and efficiently locating parking-slots using a vision-based system is still an unresolved issue. Even more seriously, there is no publicly available labeled benchmark dataset for tuning and testing parking-slot detection algorithms. In this paper, we attempt to fill the above-mentioned research gaps to some extent and our contributions are twofold. Firstly, to facilitate the study of vision-based parking-slot detection, a large-scale parking-slot image database is established. This database comprises 8600 surround-view images collected from typical indoor and outdoor parking sites. For each image in this database, the marking-points and parking-slots are carefully labeled. Such a database can serve as a benchmark to design and validate parking-slot detection algorithms. Secondly, a learning-based parking-slot detection approach, namely PSD L , is proposed. Using PSD L , given a surround-view image, the marking-points will be detected first and then the valid parking-slots can be inferred. The efficacy and efficiency of PSD L have been corroborated on our database. It is expected that PSD L can serve as a baseline when the other researchers develop more sophisticated methods.

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Vacant Parking Space Detection Based on Plane-Based Bayesian Hierarchical Framework

Cited by 56 publications

References 25 publications

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

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

Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach

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