MiniTracker: A Lightweight CNN-based System for Visual Object Tracking on Embedded Device

Conference Paper

Visual object tracking (VOT) is a computer vision application and has a wide range of use. However, related state of the art algorithms using deep learning methods, are computationally intensive and storage explosive. Whats more, despite many deep learning accelerators have been proposed, many of them are general structure. So, in this paper, we propose a lightweight CNN-based system-MiniTracker, integration of algorithm and hardware-particularly efficient for VOT. Because of the fully-convolutional Siamese network we used, the parameters of network do not need online training, which reduces computation consumptions dramatically. We adapt the original Siamese network (SN) into effective hardware implementation by parameter pruning and quantization. Then a lightweight CNN with the 8-bit parameters is produced, which is only 1.939MB. The real tracking rate is 18.6 frames per second at the cost of 1.284W on ZedBoard. Moreover, Compared with other hardware implementations, our system is robust to challenging scenarios, such as occlusions, changing appearance, illumination variations and etc.

Full Text

Duke Authors

Cited Authors

  • Zhang, B; Li, X; Han, J; Zeng, X

Published Date

  • January 31, 2019

Published In

  • International Conference on Digital Signal Processing, Dsp

Volume / Issue

  • 2018-November /

International Standard Book Number 13 (ISBN-13)

  • 9781538668115

Digital Object Identifier (DOI)

  • 10.1109/ICDSP.2018.8631813

Citation Source

  • Scopus