Real-time object tracking based on scale-invariant features employing bio-inspired hardware.
We developed a vision sensor system that performs a scale-invariant feature transform (SIFT) in real time. To apply the SIFT algorithm efficiently, we focus on a two-fold process performed by the visual system: whole-image parallel filtering and frequency-band parallel processing. The vision sensor system comprises an active pixel sensor, a metal-oxide semiconductor (MOS)-based resistive network, a field-programmable gate array (FPGA), and a digital computer. We employed the MOS-based resistive network for instantaneous spatial filtering and a configurable filter size. The FPGA is used to pipeline process the frequency-band signals. The proposed system was evaluated by tracking the feature points detected on an object in a video.
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4, Hibikino, Wakamatsu, Fukuoka, 808-0196, Japan. Electronic address: s-yasukawa@brain.kyutech.ac.jp.
Shinsuke
S
Yasukawa
Graduate School of Engineering, Osaka University, 2-1, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: h-okuno@eei.eng.osaka-u.ac.jp.
Hirotsugu
H
Okuno
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4, Hibikino, Wakamatsu, Fukuoka, 808-0196, Japan. Electronic address: ishii@brain.kyutech.ac.jp.
Kazuo
K
Ishii
Graduate School of Engineering, Osaka University, 2-1, Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: yagi@eei.eng.osaka-u.ac.jp.
Tetsuya
T
Yagi
D000465:Algorithms; D003199:Computer Systems / Q000639:trends; D003201:Computers / Q000639:trends; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000639:trends; D010775:Photic Stimulation / Q000379:methods; D012666:Semiconductors
Bio-inspired architecture; Hardware acceleration; Intelligent vision sensor; Real-time object tracking; Resistive network; Scale-invariant feature