An Object Detection and Classification Method using Radar and Camera Data Fusion

Fahad A. Jibrin; Zhenmiao Deng; Yixiong Zhang

doi:10.1109/ICSIDP47821.2019.9173452

An Object Detection and Classification Method using Radar and Camera Data Fusion

Fahad A. Jibrin, Zhenmiao Deng, Yixiong Zhang

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

13 Scopus citations

Abstract

Millimeter-wave radar has proven to have a good range estimation accuracy and is less influenced by weather conditions. However, it is difficult for radar to recognize objects, and it is prone to cause a false alarm. In this paper, we present an object detection and classification by jointly using a radar and camera sensors for traffic surveillance applications. The proposed method fuses the Regions of Interest (ROIs) generated on each of the detection results obtained independently from radar and camera sensors. Reducing the high false alarm of a radar sensor is the main aim of the fusion method. Then, a Convolutional Neural Network (CNN) is used to classify the final fused detected objects into one of the six-vehicle categories; Sedan, Truck, Minivan, Bus, Microbus, and SUV. The proposed method was verified using real data. Results obtained demonstrate the good performance of the proposed fusion approach in traffic surveillance context.

Original language	English
Title of host publication	ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728123455
DOIs	https://doi.org/10.1109/ICSIDP47821.2019.9173452
State	Published - Dec 2019
Externally published	Yes
Event	2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 - Chongqing, China Duration: 11 Dec 2019 → 13 Dec 2019

Publication series

Name	ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

Conference

Conference	2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Country/Territory	China
City	Chongqing
Period	11/12/19 → 13/12/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Gaussian mixture model (GMM)
a region of interest (ROI)
convolutional neural network (CNN)
fusion

ASJC Scopus subject areas

Instrumentation
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems
Signal Processing
Information Systems and Management

Access to Document

10.1109/ICSIDP47821.2019.9173452

Cite this

Jibrin, F. A., Deng, Z., & Zhang, Y. (2019). An Object Detection and Classification Method using Radar and Camera Data Fusion. In ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019 Article 9173452 (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSIDP47821.2019.9173452

Jibrin, Fahad A. ; Deng, Zhenmiao ; Zhang, Yixiong. / An Object Detection and Classification Method using Radar and Camera Data Fusion. ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019).

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title = "An Object Detection and Classification Method using Radar and Camera Data Fusion",

abstract = "Millimeter-wave radar has proven to have a good range estimation accuracy and is less influenced by weather conditions. However, it is difficult for radar to recognize objects, and it is prone to cause a false alarm. In this paper, we present an object detection and classification by jointly using a radar and camera sensors for traffic surveillance applications. The proposed method fuses the Regions of Interest (ROIs) generated on each of the detection results obtained independently from radar and camera sensors. Reducing the high false alarm of a radar sensor is the main aim of the fusion method. Then, a Convolutional Neural Network (CNN) is used to classify the final fused detected objects into one of the six-vehicle categories; Sedan, Truck, Minivan, Bus, Microbus, and SUV. The proposed method was verified using real data. Results obtained demonstrate the good performance of the proposed fusion approach in traffic surveillance context.",

keywords = "Gaussian mixture model (GMM), a region of interest (ROI), convolutional neural network (CNN), fusion",

author = "Jibrin, \{Fahad A.\} and Zhenmiao Deng and Yixiong Zhang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 ; Conference date: 11-12-2019 Through 13-12-2019",

year = "2019",

month = dec,

doi = "10.1109/ICSIDP47821.2019.9173452",

language = "English",

series = "ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019",

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Jibrin, FA, Deng, Z & Zhang, Y 2019, An Object Detection and Classification Method using Radar and Camera Data Fusion. in ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019., 9173452, ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019, Chongqing, China, 11/12/19. https://doi.org/10.1109/ICSIDP47821.2019.9173452

An Object Detection and Classification Method using Radar and Camera Data Fusion. / Jibrin, Fahad A.; Deng, Zhenmiao; Zhang, Yixiong.
ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 9173452 (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019).

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

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AU - Deng, Zhenmiao

AU - Zhang, Yixiong

PY - 2019/12

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N2 - Millimeter-wave radar has proven to have a good range estimation accuracy and is less influenced by weather conditions. However, it is difficult for radar to recognize objects, and it is prone to cause a false alarm. In this paper, we present an object detection and classification by jointly using a radar and camera sensors for traffic surveillance applications. The proposed method fuses the Regions of Interest (ROIs) generated on each of the detection results obtained independently from radar and camera sensors. Reducing the high false alarm of a radar sensor is the main aim of the fusion method. Then, a Convolutional Neural Network (CNN) is used to classify the final fused detected objects into one of the six-vehicle categories; Sedan, Truck, Minivan, Bus, Microbus, and SUV. The proposed method was verified using real data. Results obtained demonstrate the good performance of the proposed fusion approach in traffic surveillance context.

AB - Millimeter-wave radar has proven to have a good range estimation accuracy and is less influenced by weather conditions. However, it is difficult for radar to recognize objects, and it is prone to cause a false alarm. In this paper, we present an object detection and classification by jointly using a radar and camera sensors for traffic surveillance applications. The proposed method fuses the Regions of Interest (ROIs) generated on each of the detection results obtained independently from radar and camera sensors. Reducing the high false alarm of a radar sensor is the main aim of the fusion method. Then, a Convolutional Neural Network (CNN) is used to classify the final fused detected objects into one of the six-vehicle categories; Sedan, Truck, Minivan, Bus, Microbus, and SUV. The proposed method was verified using real data. Results obtained demonstrate the good performance of the proposed fusion approach in traffic surveillance context.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Jibrin FA, Deng Z, Zhang Y. An Object Detection and Classification Method using Radar and Camera Data Fusion. In ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 9173452. (ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019). doi: 10.1109/ICSIDP47821.2019.9173452

An Object Detection and Classification Method using Radar and Camera Data Fusion

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this