Jointly detecting and retrieving vehicles from road image sequences based on CNN

Xiao Wu; Yaochen Li; Yuehu Liu; Shanmin Pang; Le Wang; Chuan Wu; Huihui Huo

doi:10.1109/IVS.2019.8813977

Jointly detecting and retrieving vehicles from road image sequences based on CNN

Xiao Wu
, Yaochen Li
, Yuehu Liu
, Shanmin Pang
, Le Wang
, Chuan Wu
, Huihui Huo

College of Artificial Intelligence and Robotics

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, a CNN-based vehicle detection and retrieval framework is proposed for the intelligent transportation system. Firstly, the vehicle target is detected from the traffic scene. The proposed object detection method uses a fully convolutional neural network (CNN) based on SqueezeNet, which has the characteristics of real-time, high accuracy and has small model size. Secondly, an intra-class image retrieval method is presented to search vehicles which are similar to the target vehicle in the dataset. The image retrieval results can be used for traffic scenes simulation and modeling. The experiments and comparisons prove the effectiveness of our framework.

Original language	English
Title of host publication	2019 IEEE Intelligent Vehicles Symposium, IV 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2530-2535
Number of pages	6
ISBN (Electronic)	9781728105604
DOIs	https://doi.org/10.1109/IVS.2019.8813977
State	Published - Jun 2019
Event	30th IEEE Intelligent Vehicles Symposium, IV 2019 - Paris, France Duration: 9 Jun 2019 → 12 Jun 2019

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings
Volume	2019-June

Conference

Conference	30th IEEE Intelligent Vehicles Symposium, IV 2019
Country/Territory	France
City	Paris
Period	9/06/19 → 12/06/19

Access to Document

10.1109/IVS.2019.8813977

Cite this

Wu, X., Li, Y., Liu, Y., Pang, S., Wang, L., Wu, C., & Huo, H. (2019). Jointly detecting and retrieving vehicles from road image sequences based on CNN. In 2019 IEEE Intelligent Vehicles Symposium, IV 2019 (pp. 2530-2535). Article 8813977 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2019.8813977

@inproceedings{52cf2ae21cf34df1b5d390932a64f116,

title = "Jointly detecting and retrieving vehicles from road image sequences based on CNN",

abstract = "In this paper, a CNN-based vehicle detection and retrieval framework is proposed for the intelligent transportation system. Firstly, the vehicle target is detected from the traffic scene. The proposed object detection method uses a fully convolutional neural network (CNN) based on SqueezeNet, which has the characteristics of real-time, high accuracy and has small model size. Secondly, an intra-class image retrieval method is presented to search vehicles which are similar to the target vehicle in the dataset. The image retrieval results can be used for traffic scenes simulation and modeling. The experiments and comparisons prove the effectiveness of our framework.",

author = "Xiao Wu and Yaochen Li and Yuehu Liu and Shanmin Pang and Le Wang and Chuan Wu and Huihui Huo",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 30th IEEE Intelligent Vehicles Symposium, IV 2019 ; Conference date: 09-06-2019 Through 12-06-2019",

year = "2019",

month = jun,

doi = "10.1109/IVS.2019.8813977",

language = "英语",

series = "IEEE Intelligent Vehicles Symposium, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2530--2535",

booktitle = "2019 IEEE Intelligent Vehicles Symposium, IV 2019",

}

Wu, X, Li, Y, Liu, Y, Pang, S, Wang, L, Wu, C & Huo, H 2019, Jointly detecting and retrieving vehicles from road image sequences based on CNN. in 2019 IEEE Intelligent Vehicles Symposium, IV 2019., 8813977, IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., pp. 2530-2535, 30th IEEE Intelligent Vehicles Symposium, IV 2019, Paris, France, 9/06/19. https://doi.org/10.1109/IVS.2019.8813977

Jointly detecting and retrieving vehicles from road image sequences based on CNN. / Wu, Xiao; Li, Yaochen; Liu, Yuehu et al.
2019 IEEE Intelligent Vehicles Symposium, IV 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2530-2535 8813977 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Jointly detecting and retrieving vehicles from road image sequences based on CNN

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AU - Pang, Shanmin

AU - Wang, Le

AU - Wu, Chuan

AU - Huo, Huihui

PY - 2019/6

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N2 - In this paper, a CNN-based vehicle detection and retrieval framework is proposed for the intelligent transportation system. Firstly, the vehicle target is detected from the traffic scene. The proposed object detection method uses a fully convolutional neural network (CNN) based on SqueezeNet, which has the characteristics of real-time, high accuracy and has small model size. Secondly, an intra-class image retrieval method is presented to search vehicles which are similar to the target vehicle in the dataset. The image retrieval results can be used for traffic scenes simulation and modeling. The experiments and comparisons prove the effectiveness of our framework.

AB - In this paper, a CNN-based vehicle detection and retrieval framework is proposed for the intelligent transportation system. Firstly, the vehicle target is detected from the traffic scene. The proposed object detection method uses a fully convolutional neural network (CNN) based on SqueezeNet, which has the characteristics of real-time, high accuracy and has small model size. Secondly, an intra-class image retrieval method is presented to search vehicles which are similar to the target vehicle in the dataset. The image retrieval results can be used for traffic scenes simulation and modeling. The experiments and comparisons prove the effectiveness of our framework.

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T3 - IEEE Intelligent Vehicles Symposium, Proceedings

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BT - 2019 IEEE Intelligent Vehicles Symposium, IV 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 30th IEEE Intelligent Vehicles Symposium, IV 2019

Y2 - 9 June 2019 through 12 June 2019

ER -

Jointly detecting and retrieving vehicles from road image sequences based on CNN

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