Visual vocabulary processor based on binary tree architecture for real-time object recognition in full-HD resolution

Authors:
Tse-Wei Chen;Yu-Chi Su;Keng-Yen Huang;Yi-Min Tsai;Shao-Yi Chien;Liang-Gee Chen
Affiliations:
VLSI Design and Education Center, University of Tokyo, Tokyo, Japan;Graduate Institute of Electronics Engineering and Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan;Graduate Institute of Electronics Engineering and Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan;Graduate Institute of Electronics Engineering and Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan;Graduate Institute of Electronics Engineering and Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan;Graduate Institute of Electronics Engineering and Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
Venue:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Year:
2012

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Abstract

Feature matching is an indispensable process for object recognition, which is an important issue for wearable devices with video analysis functionalities. To implement a low-power SoC for object recognition, the proposed visual vocabulary processor (VVP) is employed to accelerate the speed of feature matching. The VVP can transform hundreds of 128-D SIFT vectors into a 64-D histogram for object matching by using the binary-tree-based architecture, and 16 calculators for the computations of the Euclidean distances are designed for each of the two processors in each level. A total of 126 visual words can be saved in the six-level hierarchical memory, which instantly offers the data required for the matching process, and more than 5 times of bandwidth can be saved compared with the non-binary-tree-based architecture. As a part of the recognition SoC, the VVP is implemented with the 65-nm CMOS technology, and the experimental results show that the gate count and the average power consumption are 280 K and 5.6 mW, respectively.