Name
Abstract | ||
|---|---|---|
Scale Invariant Feature Transform (SIFT) is one of the most popular and robust feature extraction algorithms for its invariance to scale, rotation and illumination. It has been widely adopted in many fields, such as video tracking, image stitching, simultaneous localization and mapping (SLAM), structure from motion (SFM) and so on. However, high computational complexity constrains its further application in real-time systems. These systems have to make a tradeoff between accuracy and performance to achieve real-time feature extraction. They adopt other faster algorithms but with less accuracy, like SURF and PCA-SIFT. In order to address this problem, this paper proposes a GPU-accelerated SIFT using CUDA, named HartSift, which realizes high-accuracy and real-time feature extraction by making full use of computing resources of CPU and GPU within a single machine. Experiments show that, on the NIVDIA GTX TITAN Black GPU, HartSift can process an image within 3.14?10.57ms (94.61?318.47fps) according to the size of images. In addition, HartSift is 59.34?75.96 times and 4.01?6.49 times faster than OpenCV-SIFT (a CPU version) and SiftGPU (a GPU version), respectively. In the mean time, HartSift's performance and CudaSIFT's (the fastest GPU version so far) are almost the same, while HartSift's accuracy is much higher than CudaSIFT's. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICPADS.2017.00029 | 2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS) |
Keywords | Field | DocType |
SIFT,GPU,high accuracy,real time,feature extraction | Structure from motion,Computer vision,Scale-invariant feature transform,Image stitching,Computer science,CUDA,Real-time computing,Feature extraction,Video tracking,Artificial intelligence,Simultaneous localization and mapping,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
1521-9097 | 978-1-5386-3208-6 | 1 |
PageRank | References | Authors |
0.35 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhihao Li | 1 | 17 | 5.10 |
| Haipeng Jia | 2 | 43 | 5.40 |
| Yunquan Zhang | 3 | 327 | 43.92 |