Algorithmic optimisation of histogram intersection kernel support vector machine‐based pedestrian detection using low complexity features

Συγγραφείς: Muhammad Bilal

Πηγή: IET Computer Vision, Vol 11, Iss 5, Pp 350-357 (2017)

Θεματικοί όροι: QA76.75-76.765, low complexity feature detection framework, SVM, Computer applications to medicine. Medical informatics, R858-859.7, 0202 electrical engineering, electronic engineering, information engineering, pedestrian detection, Computer software, 02 engineering and technology, algorithmic optimisation, image frame, 6. Clean water, histogram intersection kernel support vector machine

Σύνδεσμος πρόσβασης: https://doaj.org/article/4950ae0eced84ad696439cee7b85576b
https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2016.0403
https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0403
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-cvi.2016.0403
https://doi.org/10.1049/iet-cvi.2016.0403
https://dblp.uni-trier.de/db/journals/iet-cvi/iet-cvi11.html#000117