Object Recognition in a Context-Aware Application

Research output: A Conference proceeding or a Chapter in BookConference contribution

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Abstract

In a dynamic operational environment such as robotic or an autonomous navigation system, the interactions between humans and objects around them play an important role (context-awareness). The task of recognizing and tracking such objects introduces many challenges in the machine vision research field. In this paper, we propose a novel method that combines the information from modern depth sensors with conventional machine vision techniques such as Scale-invariant Feature Transform (SIFT) to produce a system that is capable of performing object recognition and tracking with a satisfactory level of accuracy in real-time. A prototype is implemented and tested to confirm that the proposed method does provide better performance comparing with currently used methods in image processing.
Original languageEnglish
Title of host publicationThe 2013 International Joint Conference on Neural Networks (IJCNN)
EditorsPlaman Angelov, Daniel Levine
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2817-2824
Number of pages8
Volume1
ISBN (Electronic)9781467361293
DOIs
Publication statusPublished - 4 Aug 2013
Event2013 International Joint Conference on Neural Networks (IJCNN) - Dallas, Texas, United States
Duration: 4 Aug 20139 Aug 2013

Conference

Conference2013 International Joint Conference on Neural Networks (IJCNN)
CountryUnited States
CityTexas
Period4/08/139/08/13

Fingerprint

Object recognition
Computer vision
Navigation systems
Robotics
Image processing
Mathematical transformations
Sensors

Cite this

VO, T., TRAN, D., & MA, W. (2013). Object Recognition in a Context-Aware Application. In P. Angelov, & D. Levine (Eds.), The 2013 International Joint Conference on Neural Networks (IJCNN) (Vol. 1, pp. 2817-2824). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2013.6707099
VO, Tan ; TRAN, Dat ; MA, Wanli. / Object Recognition in a Context-Aware Application. The 2013 International Joint Conference on Neural Networks (IJCNN). editor / Plaman Angelov ; Daniel Levine. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 2817-2824
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abstract = "In a dynamic operational environment such as robotic or an autonomous navigation system, the interactions between humans and objects around them play an important role (context-awareness). The task of recognizing and tracking such objects introduces many challenges in the machine vision research field. In this paper, we propose a novel method that combines the information from modern depth sensors with conventional machine vision techniques such as Scale-invariant Feature Transform (SIFT) to produce a system that is capable of performing object recognition and tracking with a satisfactory level of accuracy in real-time. A prototype is implemented and tested to confirm that the proposed method does provide better performance comparing with currently used methods in image processing.",
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VO, T, TRAN, D & MA, W 2013, Object Recognition in a Context-Aware Application. in P Angelov & D Levine (eds), The 2013 International Joint Conference on Neural Networks (IJCNN). vol. 1, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 2817-2824, 2013 International Joint Conference on Neural Networks (IJCNN), Texas, United States, 4/08/13. https://doi.org/10.1109/IJCNN.2013.6707099

Object Recognition in a Context-Aware Application. / VO, Tan; TRAN, Dat; MA, Wanli.

The 2013 International Joint Conference on Neural Networks (IJCNN). ed. / Plaman Angelov; Daniel Levine. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 2817-2824.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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VO T, TRAN D, MA W. Object Recognition in a Context-Aware Application. In Angelov P, Levine D, editors, The 2013 International Joint Conference on Neural Networks (IJCNN). Vol. 1. USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 2817-2824 https://doi.org/10.1109/IJCNN.2013.6707099