T. Stūrmanis, R. Novickis . An efficient FPGA-based co-processor for feature point detection and tracking. Proceedings - 2021 24th Euromicro Conference on Digital System Design, 2021.
Bibtex citation:
Bibtex citation:
@inproceedings{12077_2021,
author = {T. Stūrmanis and R. Novickis },
title = {An efficient FPGA-based co-processor for feature point detection and tracking},
journal = {Proceedings - 2021 24th Euromicro Conference on Digital System Design},
year = {2021}
}
author = {T. Stūrmanis and R. Novickis },
title = {An efficient FPGA-based co-processor for feature point detection and tracking},
journal = {Proceedings - 2021 24th Euromicro Conference on Digital System Design},
year = {2021}
}
Abstract: The use of mobile agents is propagating throughout various industries. Nevertheless, the success of novel applications relies on the utilization of novel computing platforms and algorithms, including acceleration technology and onboard localization. We propose an FPGA-based sparse optical flow computing accelerator based on the FAST feature detection and BRIEF feature descriptor. The correspondences are found by splitting the image into static regions, where for each region, the feature points are tracked in-between the frames. The accelerator is fully pipelined and achieves a performance of 300 fps with VGA resolution images. The experimentation with the default configuration of the accelerator shows to support a reliable measurement of frame-to-frame image plane rotation of 9 degrees and translation of 24 pixels, with the total error below 0.4 degrees and 0.16 pixels.