In-vehicle object detection in the wild for driverless vehicles. / Dinakaran, Ranjith ; Zhang, Li; Jiang, Richard .

World Scientific Proceedings Series on Computer Engineering and Information Science. In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12. : World Scientific, Singapore, 2020. p. 1139-1147.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

In-vehicle object detection in the wild for driverless vehicles. / Dinakaran, Ranjith ; Zhang, Li; Jiang, Richard .

World Scientific Proceedings Series on Computer Engineering and Information Science. In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12. : World Scientific, Singapore, 2020. p. 1139-1147.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Dinakaran, R, Zhang, L & Jiang, R 2020, In-vehicle object detection in the wild for driverless vehicles. in World Scientific Proceedings Series on Computer Engineering and Information Science. World Scientific, Singapore, In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12., pp. 1139-1147. https://doi.org/10.1142/9789811223334_0136

APA

Dinakaran, R., Zhang, L., & Jiang, R. (2020). In-vehicle object detection in the wild for driverless vehicles. In World Scientific Proceedings Series on Computer Engineering and Information Science (pp. 1139-1147). World Scientific, Singapore. https://doi.org/10.1142/9789811223334_0136

Vancouver

Dinakaran R, Zhang L, Jiang R. In-vehicle object detection in the wild for driverless vehicles. In World Scientific Proceedings Series on Computer Engineering and Information Science. In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12.: World Scientific, Singapore. 2020. p. 1139-1147 https://doi.org/10.1142/9789811223334_0136

Author

Dinakaran, Ranjith ; Zhang, Li ; Jiang, Richard . / In-vehicle object detection in the wild for driverless vehicles. World Scientific Proceedings Series on Computer Engineering and Information Science. In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12. : World Scientific, Singapore, 2020. pp. 1139-1147

BibTeX

@inproceedings{739b8f42b8de47928ce44ff932801b2c,
title = "In-vehicle object detection in the wild for driverless vehicles",
abstract = "In-vehicle human object identification plays an important role in vision-based automated vehicle driving systems while objects such as pedestrians and vehicles on roads or streets are the primary targets to protect from driverless vehicles. A challenge is the difficulty to detect objects in moving under the wild conditions, while illumination and image quality could drastically vary. In this work, to address this challenge, we exploit Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) to handle with the wild conditions. In our work, a GAN was trained with low-quality images to handle with the challenges arising from the wild conditions in smart cities, while a cascaded SSD is employed as the object detector to perform with the GAN. We used tested our approach under wild conditions using taxi driver videos on London street in both daylight and night times, and the tests from in-vehicle videos demonstrate that this strategy can drastically achieve a better detection rate under the wild conditions.",
author = "Ranjith Dinakaran and Li Zhang and Richard Jiang",
year = "2020",
month = aug,
day = "15",
doi = "10.1142/9789811223334_0136",
language = "English",
pages = "1139--1147",
booktitle = "World Scientific Proceedings Series on Computer Engineering and Information Science",
publisher = "World Scientific, Singapore",

}

RIS

TY - GEN

T1 - In-vehicle object detection in the wild for driverless vehicles

AU - Dinakaran, Ranjith

AU - Zhang, Li

AU - Jiang, Richard

PY - 2020/8/15

Y1 - 2020/8/15

N2 - In-vehicle human object identification plays an important role in vision-based automated vehicle driving systems while objects such as pedestrians and vehicles on roads or streets are the primary targets to protect from driverless vehicles. A challenge is the difficulty to detect objects in moving under the wild conditions, while illumination and image quality could drastically vary. In this work, to address this challenge, we exploit Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) to handle with the wild conditions. In our work, a GAN was trained with low-quality images to handle with the challenges arising from the wild conditions in smart cities, while a cascaded SSD is employed as the object detector to perform with the GAN. We used tested our approach under wild conditions using taxi driver videos on London street in both daylight and night times, and the tests from in-vehicle videos demonstrate that this strategy can drastically achieve a better detection rate under the wild conditions.

AB - In-vehicle human object identification plays an important role in vision-based automated vehicle driving systems while objects such as pedestrians and vehicles on roads or streets are the primary targets to protect from driverless vehicles. A challenge is the difficulty to detect objects in moving under the wild conditions, while illumination and image quality could drastically vary. In this work, to address this challenge, we exploit Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) to handle with the wild conditions. In our work, a GAN was trained with low-quality images to handle with the challenges arising from the wild conditions in smart cities, while a cascaded SSD is employed as the object detector to perform with the GAN. We used tested our approach under wild conditions using taxi driver videos on London street in both daylight and night times, and the tests from in-vehicle videos demonstrate that this strategy can drastically achieve a better detection rate under the wild conditions.

U2 - 10.1142/9789811223334_0136

DO - 10.1142/9789811223334_0136

M3 - Conference contribution

SP - 1139

EP - 1147

BT - World Scientific Proceedings Series on Computer Engineering and Information Science

PB - World Scientific, Singapore

CY - In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12.

ER -