SRI-SURF: A better SURF powered by scaled-RAM interpolator on FPGA

摘要

Speed-Up Robust Feature (SURF) is an effective algorithm for feature extraction. We propose a novel Scaled-RAM Interpolator (SRI) on FPGA to deal with the high complexity of SURF by introducing two methods. 1) Interpolation of Integral Image (I3) restores the sub-pixel details of image to improve matching precision, and halves the memory access to achieve acceleration; 2) Multi-Scaled RAM (MSR) normalizes the storage allocation by scale to decrease complexity of memory access and reject redundant memory. With SRI, our system achieves better matching precision, higher processing speed, and lower storage occupation. The results of evaluation implemented on Stratix III EP3SL340 FPGA show that 1) our SRI-SURF system performs better in matching than OpenSURF; 2) the system is capable to process 241K feature points per second (PPS), which is about 7× of previous work on FPGA and is comparable to the recent ASIC solution; 3) the maximal frame rate reaches 488FPS at VGA and 72FPS at 1080P, which outperforms designs in other publications; 4) our design is compact, which only occupies about 22% logic resource and about 43% RAM resource at 1080P. The proposed SRI-SURF meets the demand of real-time embedded applications with better precision at high resolution.