Day and night-time drive analysis using stereo vision for naturalistic driving studies

Philipsen, Mark Philip; Jensen, Morten Bornø; Satzoda, Ravi Kumar; Trivedi, Mohan M.; Møgelmose, Andreas; Moeslund, Thomas B.

doi:10.1109/ivs.2015.7225850

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…tail lights and reflections. In [76] vehicle detection at night is assisted by a stereo vision 3D edges extractor, while in [77], vehicle detection rely solely on stereo vision for both day-and nighttime data.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives

Jensen

Philipsen

Møgelmose

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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183

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Section: Discussion and Perspectivesmentioning

confidence: 99%

Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives

Jensen

Philipsen

Møgelmose

et al. 2016

IEEE Trans. Intell. Transport. Syst.

Self Cite

183

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“…Computer vision-based traffic sign detection and recognition have been studied for some purposes, such as Advanced Driver Assistance Systems (ADAS) [34,35], Auto Driving show the fast detection speed while struggling to precisely localize small objects because they both divide images into many grids that contain perhaps two or smaller objects. Faster R-CNN, the two-stage object detection framework, shows higher accuracy than Yolo and SSD [28].…”

Section: Introductionmentioning

confidence: 99%

Weight analysis for various prohibitory sign detection and recognition using deep learning

Dewi

Chen

Yu³

2020

Multimed Tools Appl

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Traffic sign recognition is meaningful for real-world applications such as self-sufficient driving, traffic surveillance, and driver safety. However, traffic sign recognition is a hard problem because different sizes, illuminations, and noises affect the sign detection and recognition. This work recognizes Taiwan's prohibitory signs using deep learning methods. First, we develop a traffic sign database since there is no such kind of database available in Taiwan. Next, we adopt three different You Only Look Once (Yolo) networks (Yolo A, Yolo B, and Yolo C) and three various Yolo V3 SPP networks (Yolo D, Yolo E, and Yolo F) for prohibitory sign recognition. Finally, we conduct the comparative experiment of Yolo V3 and Yolo V3 SPP with different weights provided by the darknet framework (the best weight, the final weight, and the last weight). Experimental results show that the mean average precision (mAP) observation of all models that the Yolo V3 SPP is better than other models. Yolo D took the optimum average accuracy at 99.0%, followed by Yolo E and Yolo F 98.9%. The accuracy of Yolo V3 SPP is growing within the detection time, but it needs more time to identify the sign.

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“…tail lights and reflections. In [77] vehicle detection at night is assisted by a stereo vision 3D edges extractor, while in [78], vehicle detection rely solely on stereo vision for both day-and nighttime data." [1] "Less than half of the TLR papers include tracking.…”

Section: Traffic Light Datasetmentioning

confidence: 99%

Building Brains for Visual Traffic Analysis

Jensen

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Day and night-time drive analysis using stereo vision for naturalistic driving studies

Cited by 9 publications

References 29 publications

Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives

Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives

Weight analysis for various prohibitory sign detection and recognition using deep learning

Building Brains for Visual Traffic Analysis

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