The object detection framework developed by Viola and Jones has become very popular due to its high quality and detection speed. However, the complexity of the computation required to train a detector makes it difficult to develop and test potential improvements to this algorithm or train detectors in the field.
In this thesis, a configurable, high-performance FPGA architecture is presented to accelerate this training process. The architecture, structured as a systolic array of pipelined compute engines, is constructed to provide high throughput and make efficient use of the available external memory bandwidth. Extensions to the Viola-Jones detection framework are implemented to demonstrate the flexibility of the architecture. The design is implemented on a Xilinx ML605 development platform running at 200~MHz and obtains a 15-fold speed-up over a multi-threaded OpenCV implementation running on a high-end processor.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/33294 |
| Date | 20 November 2012 |
| Creators | Lo, Charles |
| Contributors | Chow, Paul |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0023 seconds