Accurate detection of driver urgency using state-of-the-art supervised and unsupervised classification algorithms
This study is to determine the factors affect the accuracy of detection of driver face urgency situations under 2 different of State-of-the-Art classification algorithms, which are supervised and unsupervised.
Main Author: | Kong,Yuanjie |
---|---|
Other Authors: | Su Rong |
Format: | Final Year Project (FYP) |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/167924 |
Similar Items
-
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
by: Leong, Shi Xiang
Published: (2017) -
Clustering and semi-supervised classification with application to driver distraction detection
by: Liu, Tianchi
Published: (2018) -
Nonparametric Bayesian methods for supervised and unsupervised learning
by: Mansinghka, Vikash Kumar
Published: (2010) -
Learning spike time codes through supervised and unsupervised structural plasticity
by: Roy, Subhrajit
Published: (2016) -
A system for unsupervised color based object classification
by: Preer, Stephen Randolph, 1976-
Published: (2013)