Accelerating SURF detector on mobile devices

Abstract

Running a SURF (Speeded Up Robust Features) detector on mobile devices remains too slow to support emerging applications such as mobile augmented reality. Porting it without adapting the algorithm to account for mobile platform limitations could result in significant runtime degradation. In this paper, we identify two mismatches between the SURF algorithm and the mobile hardware that cause substantial slow-down of the point detection process: 1) mismatch between the data access pattern and the small cache size, and 2) mismatch between the huge amount of branches and high pipeline hazard penalty. To address the mismatches, we propose two techniques: tiled SURF and gradient moment based orientation assignment. Tiled SURF improves data locality and greatly reduces memory traffic. A method for determining the optimal tile sizes, named content-aware tiling, is designed to minimize runtime and maximize detection accuracy. To avoid the penalties caused by pipeline hazards, we replace the original orientation operator with branching-free gradient moment computations. The proposed techniques are tested on three mobile platforms. Comparing to the original SURF, the accelerated SURF achieves a 6x~8x speedup without sacrificing recognition accuracy. Meanwhile, it achieves 59%~80% reductions in the runtime ratio of the detector running on mobile platforms compared with on x86-based PCs.