Comparison of Feature Extractors for Real-Time Object Detection on Android Smartphone

Saipullah, Khairul Muzzammil (2013) Comparison of Feature Extractors for Real-Time Object Detection on Android Smartphone. Journal of Theoretical and Applied Information Technology . pp. 135-142. ISSN 1992-8645

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This paper presents the analysis of real-time object detection method for embedded system particularly the Android smartphone. As we all know, object detection algorithm is a complicated algorithm that consumes high performance hardware to execute the algorithm in real time. However due to the development of embedded hardware and object detection algorithm, current embedded device may be able to execute the object detection algorithm in real-time. In this study, we analyze the best object detection algorithm with respect to efficiency, quality and robustness of the algorithm. Several object detection algorithms have been compared such as Scale Invariant Feature Transform (SIFT), Speeded-Up Feature Transform (SuRF), Center Surrounded External (CenSurE), Good Features To Track (GFTT), Maximally-Stable External Region Extractor (MSER), Oriented Binary Robust Independent Elementary Features (ORB), and Features from Accelerated Segment Test (FAST) on the GalaxyS Android smartphone. The results show that FAST algorithm has the best combination of speed and object detection performance.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Electronics and Computer Engineering > Department of Computer Engineering
Depositing User: Engr. Khairul Muzzammil Saipullah
Date Deposited: 12 Jul 2013 09:16
Last Modified: 28 May 2015 03:57
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