The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
Online ISSN : 2424-3124
2022
Session ID : 2P1-R03
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MobileGoturns: Light-Weight Deep Regression Networks for High-Speed Visual Feedback System
Chanrathnak BORANN*Hiromasa OKU
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Abstract

We present a light-weight CNN-based object tracker called MobileGoturn. The model resembles GOTURN except that convolutional layers are replaced by a low-cost structure called depthwise separable convolution and a hyper-parameter known as width-multiplier were applied to make a trade-off between speed and accuracy. We incorporated our novel model into a high-speed visual feedback system and demonstrated that the model is comparatively faster to track our proposed target objects in real-time.

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© 2022 The Japan Society of Mechanical Engineers
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