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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.


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.


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.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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