@Article{jbd.2020.01003,
AUTHOR = {Yanqiu Cao, Chao Tan, Genlin Ji},
TITLE = {A Multi-Label Classification Method for Vehicle Video},
JOURNAL = {Journal on Big Data},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {19--31},
URL = {http://www.techscience.com/jbd/v2n1/40128},
ISSN = {2579-0056},
ABSTRACT = {In the last few years, smartphone usage and driver sleepiness have been 
unanimously considered to lead to numerous road accidents, which causes many scholars 
to pay attention to autonomous driving. For this complexity scene, one of the major 
challenges is mining information comprehensively from massive features in vehicle video. 
This paper proposes a multi-label classification method MCM-VV (Multi-label 
Classification Method for Vehicle Video) for vehicle video to judge the label of road 
condition for unmanned system. Method MCM-VV includes a process of feature 
extraction and a process of multi-label classification. During feature extraction, grayscale, 
lane line and the edge of main object are extracted after video preprocessing. During the 
multi-label classification, the algorithm DR-ML-KNN (Multi-label K-nearest Neighbor 
Classification Algorithm based on Dimensionality Reduction) learns the training set to 
obtain multi-label classifier, then predicts the label of road condition according to 
maximum a posteriori principle, finally outputs labels and adds the new instance to 
training set for the optimization of classifier. Experimental results on five vehicle video 
datasets show that the method MCM-VV is effective and efficient. The DR-ML-KNN 
algorithm reduces the runtime by 50%. It also reduces the time complexity and improves 
the accuracy.},
DOI = {10.32604/jbd.2020.01003}
}