Name
Abstract | ||
|---|---|---|
In this paper, we introduce a novel architecture of an engine for high performance multi-scale detection of objects in videos based on WaldBoost training algorithm. The key properties of the architecture include processing of streamed data and low resource consumption. We implemented the engine in FPGA and we show that it can process 640×480 pixel video streams at over 160 fps without the need of external memory. We evaluate the design on the face detection task, compare it to state of the art designs, and discuss its features and limitations. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FPL.2013.6645559 | Field Programmable Logic and Applications |
Keywords | Field | DocType |
face recognition,field programmable gate arrays,object detection,video streaming,FPGA,WaldBoost training algorithm,face detection task,high performance architecture,high performance multiscale detection,object detection,pixel video streams,resource consumption,streamed data processing,streamed videos | Object detection,Facial recognition system,Architecture,Object-class detection,Computer science,Field-programmable gate array,Real-time computing,Pixel,Face detection,Auxiliary memory | Conference |
ISSN | Citations | PageRank |
1946-1488 | 4 | 0.55 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pavel Zemcík | 1 | 120 | 24.73 |
| Roman Juránek | 2 | 58 | 11.64 |
| Petr Musil | 3 | 6 | 2.30 |
| Martin Musil | 4 | 6 | 2.30 |
| Michal Hradis | 5 | 132 | 14.19 |