Robust Hierarchical Deep Learning for Vehicular Management

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

47 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)4148-4156
Journal / PublicationIEEE Transactions on Vehicular Technology
Volume68
Issue number5
Online published23 Nov 2018
Publication statusPublished - May 2019

Abstract

Congestion detection is an important aspect of Vehicular Management. However, most of the existing algorithms are insufficient for real applications. Traditional features are not discriminative which results in rather poor performance under complex scenarios. The deep features can better represent high-level information, but the training of deep network for regression is difficult. To promote the congestion detection, a robust hierarchical deep learning is proposed for the task. In this method, a deep network is designed for hierarchical semantic feature extraction. Different from traditional deep regression networks which usually directly utilize mean squared error as loss function, a robust metric learning is employed to effectively train the network. Based on this, multiple networks are combined together to further improve the generalization ability. Extensive experiments are conducted and the proposed model is confirmed to be effective.

Research Area(s)

  • congestion detection, Convolutional neural networks, crowd counting, Deep learning, ensemble learning, Feature extraction, Measurement, metric learning, regression, Task analysis, traffic surveillance, Training, Videos

Citation Format(s)

Robust Hierarchical Deep Learning for Vehicular Management. / Wang, Qi; Wan, Jia; Li, Xuelong.
In: IEEE Transactions on Vehicular Technology, Vol. 68, No. 5, 05.2019, p. 4148-4156.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review