Awesome
PCA-RECT: Event-based Object Detection and Classification
The VLFeat Library is provided with the repo and the MATLAB script configures it on-the-fly.
Needs MATLAB AER Vision Functions from Garrick Orchard.
There are four versions of the code:
FPGAModular: For evaluating the exact modular version where FPGA is closely followed.
FAST: 100x faster Quick floating-point versions for parameter testing.
FASTnoPCA: Fast version without principal component analysis.
FASTnoPCAwithDet: Fast version with detector incorporated (no PCA).
The training files can be found in the N-SOD Dataset and needs to be placed in the correct path, relative to the main executing file. (the code uses '../' to reference the files).
../N-SOD Datatet/
Instructions to execute
- Download the N-SOD Dataset and place above the PCA-RECT folder.
- Add to path the MATLAB AER Vision Functions.
- Run one of the scripts, e.g. Event_context_DEMOuav_rmax7by7rect_FAST
Instructions for Tunning Parameters (testing)
Tune descriptor size:
- Set value of "param.descsize=7"
- CTRL+H to replace "5by5" to "7by7"
Tune codebook size:
- Set value of "histopts.num_bins=150"
- CTRL+H to replace "100codebok" to "150codebok"
Then, you can run the code to load or get your data properly.
Citations
Ramesh B., Ussa A., Vedova L.D., Yang H., Orchard G. (2020) Low-Power Dynamic Object Detection and Classification With Freely Moving Event Cameras. Front. Neurosci. 14:135 doi: 10.3389/fnins.2020.00135
@ARTICLE{10.3389/fnins.2020.00135,
AUTHOR="Ramesh, Bharath and Ussa, Andrés and Della Vedova, Luca and Yang, Hong and Orchard, Garrick",
TITLE="Low-Power Dynamic Object Detection and Classification With Freely Moving Event Cameras",
JOURNAL="Frontiers in Neuroscience",
VOLUME="14",
PAGES="135",
YEAR="2020",
DOI="10.3389/fnins.2020.00135",
ISSN="1662-453X"
}
Ramesh B., Ussa A., Vedova L.D., Yang H., Orchard G. (2019) PCA-RECT: An Energy-Efficient Object Detection Approach for Event Cameras. In: Carneiro G., You S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science, vol 11367. Springer, Cham
@InProceedings{10.1007/978-3-030-21074-8_35,
author="Ramesh, Bharath and Ussa, Andr{\'e}s and Vedova, Luca Della and Yang, Hong and Orchard, Garrick",
editor="Carneiro, Gustavo and You, Shaodi",
title="PCA-RECT: An Energy-Efficient Object Detection Approach for Event Cameras",
booktitle="Computer Vision -- ACCV 2018 Workshops",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="434--449",
isbn="978-3-030-21074-8"
}