LIDAR and vision-based real-time traffic sign detection and recognition algorithm for intelligent vehicle

Zhou, Lipu; Deng, Zhidong

doi:10.1109/itsc.2014.6957752

Cited by 63 publications

(33 citation statements)

References 18 publications

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“…Similarly, a supervoxel neighborhood-based Hough forest framework [30] was also proposed to detect traffic signposts. In addition, a LiDAR and vision-based real-time traffic signpost detection method [31] was developed for intelligent vehicle applications.…”

Section: B Traffic Signpost Detectionmentioning

confidence: 99%

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Guan

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a novel algorithm for detecting road scene objects (e.g., light poles, traffic signposts, and cars) from 3-D mobile-laser-scanning point cloud data for transportationrelated applications. To describe local abstract features of point cloud objects, a contextual visual vocabulary is generated by integrating spatial contextual information of feature regions. Objects of interest are detected based on the similarity measures of the bag of contextual-visual words between the query object and the segmented semantic objects. Quantitative evaluations on two selected data sets show that the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.949, 0.970, 0.922, and 0.959, respectively, in detecting light poles, traffic signposts, and cars. Comparative studies demonstrate the superior performance of the proposed algorithm over other existing methods.

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Section: B Traffic Signpost Detectionmentioning

confidence: 99%

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Guan

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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“…A system that combines LiDAR and Cameras can improve the sign detection and recognition as it has the advantages and the information of both sources. [60] trains a SVM with 10 variables: 9 of different color spaces provided by the camera (RGB, HSV, CIEL*a*b*) plus reflection intensity observed by LiDAR. After verifying the 3D geometry of detected signs, a linear SVM classifier is applied to HOG features.…”

Section: ) Sensors Fusion Solutionsmentioning

confidence: 99%

A Review of Sensor Technologies for Perception in Automated Driving

Martí

Miguel

Fernández

et al. 2019

IEEE Intell. Transport. Syst. Mag.

238

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After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show the intention of the market in sensors technologies for Automated Vehicles.

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“…Since 3D LIDAR point cloud is dense, prior information on the location of the road sign is of key importance. For example, in [20], a vision-based system is used to detect the road limits. Indeed, since road signs are assumed to be located outside road boundaries, road points are removed from the cloud and only LIDAR points that are outside the road boundaries are used for the road sign detection process.…”

Section: Detection Of Road Signsmentioning

confidence: 99%