Open Access
ARTICLE
A Multi-Label Classification Method for Vehicle Video
Yanqiu Cao1, Chao Tan1, Genlin Ji1, *
1 School of Computer Science and Technology, Nanjing Normal University, Nanjing, China.
* Corresponding Author: Genlin Ji. Email: .
Journal on Big Data 2020, 2(1), 19-31. https://doi.org/10.32604/jbd.2020.01003
Received 05 January 2020; Accepted 13 May 2020; Issue published 07 September 2020
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.
Keywords
Cite This Article
Y. Cao, C. Tan and G. Ji, "A multi-label classification method for vehicle video,"
Journal on Big Data, vol. 2, no.1, pp. 19–31, 2020. https://doi.org/10.32604/jbd.2020.01003