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Open AccessArticle

Fast Visual Tracking Based on Convolutional Networks

1
Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan
2
Ship and Ocean Industries R&D Center (SOIC), New Taipei City 25170, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(8), 2405; https://doi.org/10.3390/s18082405
Received: 31 May 2018 / Revised: 19 July 2018 / Accepted: 19 July 2018 / Published: 24 July 2018
(This article belongs to the Special Issue Selected Sensor Related Papers from ICI2017)
Recently, an upsurge of deep learning has provided a new direction for the field of computer vision and visual tracking. However, expensive offline training time and the large number of images required by deep learning have greatly hindered progress. This paper aims to further improve the computational performance of CNT which is reported to deliver 5 fps performance in visual tracking, we propose a method called Fast-CNT which differs from CNT in three aspects: firstly, an adaptive k value (rather than a constant 100) is determined for an input video; secondly, background filters used in CNT are omitted in this work to save computation time without affecting performance; thirdly, SURF feature points are used in conjunction with the particle filter to address the drift problem in CNT. Extensive experimental results on land and undersea video sequences show that Fast-CNT outperforms CNT by 2~10 times in terms of computational efficiency. View Full-Text
Keywords: visual tracking; convolutional networks; clustering; IoT; object detection visual tracking; convolutional networks; clustering; IoT; object detection
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MDPI and ACS Style

Huang, R.-J.; Tsao, C.-Y.; Kuo, Y.-P.; Lai, Y.-C.; Liu, C.C.; Tu, Z.-W.; Wang, J.-H.; Chang, C.-C. Fast Visual Tracking Based on Convolutional Networks. Sensors 2018, 18, 2405.

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