Parking assistance using dense motion-stereo

Unger, Christian; Wahl, Eric; Ilić, Slobodan

doi:10.1007/s00138-011-0385-1

Cited by 30 publications

(18 citation statements)

References 21 publications

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“…Imaging sensors have also been widely used in this approach, with the majority of imaging sensor-based methods reconstructing 3-D structures of parking spaces and recognizing adjacent vehicles using 3-D point clouds. Various techniques have been used for 3-D reconstruction tasks including monocular motion stereo [11]- [13], binocular stereo [14], [15], and structured light [10]. Unlike these methods, the method in [16] directly recognizes adjacent vehicles by utilizing horizontal edges.…”

Section: A Free Space-based Approachmentioning

confidence: 99%

Automatic Parking Space Detection and Tracking for Underground and Indoor Environments

Suhr

Jung

2016

IEEE Trans. Ind. Electron.

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Even though many public parking lots are located underground and indoors, most existing automatic parking space detection and tracking methods have difficulty handling such scenarios due to severe illumination and complex obstacle conditions. To overcome this problem, this paper proposes a method that detects and tracks parking spaces in underground and indoor environments by fusing sensors already mounted on mass produced vehicles. The proposed detection method finds parking spaces based on a high-level fusion of two complementary approaches: parking slot marking-based and free space-based. Parking slots are detected by estimating parallel line pairs and free spaces are detected by recognizing the positions of parked vehicles as well as pillars. The proposed tracking method enhances the previous method by considering pillar information. Since pillars degrade parking slot tracking performance, this method estimates a pillar region and utilizes it to remove false edges and to estimate the amount of occlusion.Index Terms-Automatic parking system, Parking space detection and tracking, Underground and indoor, Sensor fusion. Jae Kyu Suhr (M'12) received his BS degree in electronic engineering at

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Section: A Free Space-based Approachmentioning

confidence: 99%

Automatic Parking Space Detection and Tracking for Underground and Indoor Environments

Suhr

Jung

2016

IEEE Trans. Ind. Electron.

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“…In the initial stage, this technique adopted stereo vision technology using two cameras [10], and nowadays, motion-stereo vision technology based on a mono camera is also used [11]- [12]. This technology is divided into feature-based sparse reconstruction [11] and dense reconstruction [12]. The dense reconstruction technology is known for its superior performance [12].…”

Section: Related Workmentioning

confidence: 99%

“…This technology is divided into feature-based sparse reconstruction [11] and dense reconstruction [12]. The dense reconstruction technology is known for its superior performance [12].…”

Section: Related Workmentioning

confidence: 99%

“…HM generation using dense-motion stereo technology commonly consists of the following four steps: obtaining a rectified stereo image pair at the previous and the present time using structure from motion (SFM), estimating a disparity map, mapping 3D points obtained by triangulation into a grid map, and map fusion to improve the quality of the map [12].…”

Section: Hm Buildingmentioning

confidence: 99%

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Parking Space Recognition for Autonomous Valet Parking Using Height and Salient-Line Probability Maps

Han¹,

Choi²

2015

ETRI J

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An autonomous valet parking (AVP) system is designed to locate a vacant parking space and park the vehicle in which it resides on behalf of the driver, once the driver has left the vehicle. In addition, the AVP is able to direct the vehicle to a location desired by the driver when requested. In this paper, for an AVP system, we introduce technology to recognize a parking space using image sensors. The proposed technology is mainly divided into three parts. First, spatial analysis is carried out using a height map that is based on dense motion stereo. Second, modelling of road markings is conducted using a probability map with a new salient‐line feature extractor. Finally, parking space recognition is based on a Bayesian classifier. The experimental results show an execution time of up to 10 ms and a recognition rate of over 99%. Also, the performance and properties of the proposed technology were evaluated with a variety of data. Our algorithms, which are part of the proposed technology, are expected to apply to various research areas regarding autonomous vehicles, such as map generation, road marking recognition, localization, and environment recognition.

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“…This is the most widely used approach as it can be implemented using various range-finding sensors, such as ultrasonic sensors [9][10][11][12][13][14][15], laser scanners [16][17][18], short-range radars [19][20][21], structured light [22], depth cameras [23], stereo cameras [24][25][26][27][28][29], etc. However, the free-space-based approach has an inherent drawback in that it cannot find free spaces when there is no adjacent vehicles and its accuracy depends on the positions and poses of adjacent 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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Parking assistance using dense motion-stereo

Cited by 30 publications

References 21 publications

Automatic Parking Space Detection and Tracking for Underground and Indoor Environments

Automatic Parking Space Detection and Tracking for Underground and Indoor Environments

Parking Space Recognition for Autonomous Valet Parking Using Height and Salient-Line Probability Maps

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

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