Name
Abstract | ||
|---|---|---|
Pedestrian detection is one of the key problems in emerging self-driving car industry. And HOG algorithm has proven to provide good accuracy for pedestrian detection. There are plenty of research works have been done in accelerating HOG algorithm on FPGA because of its low-power and high-throughput characteristics. In this paper, we present a high-performance HOG architecture for pedestrian detection on a low-cost FPGA platform. It achieves a maximum throughput of 526 FPS with 640x480 input images, which is 3.25 times faster than the state of the art design. The accelerator is integrated with SVM-based prediction in realizing a pedestrian detection system. And the power consumption of the whole system is comparable with the best existing implementations. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Architecture,Computer science,Support vector machine,Field-programmable gate array,Implementation,Artificial intelligence,Extractor,Throughput,Computer hardware,Pedestrian detection,Machine learning,Power consumption |
DocType | Volume | Citations |
Journal | abs/1802.02187 | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vinh Ngo | 1 | 0 | 0.68 |
| Arnau Casadevall | 2 | 0 | 0.68 |
| Marc Codina | 3 | 0 | 1.01 |
| David Castells-Rufas | 4 | 45 | 7.70 |
| Jordi Carrabina | 5 | 139 | 36.98 |