Real-time object tracking based on scale-invariant features employing bio-inspired hardware

• We developed a real-time vision system with analog/digital mixed architecture.
• The system consists of an analog MOS transistor resistive network (RN) and an FPGA.
• The RN conducts multi-scale filtering in real time with a low power consumption.
• The FPGA finds scale-invariant key points by frequency-band parallel processing.
• The system was combined with a PC to track a moving target of a varying scale.

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.