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author:

Yu, Y. (Yu, Y..) [1] | Xu, M. (Xu, M..) [2] | Gu, J. (Gu, J..) [3]

Indexed by:

Scopus

Abstract:

Vision-based traffic accident detection is one of the challenging tasks in intelligent transportation systems due to the multi-modalities of traffic accidents. The first challenging issue is about how to learn robust and discriminative spatio-temporal feature representations. Since few training samples of traffic accidents can be collected, sparse coding techniques can be used for small data case. However, most sparse coding algorithms which use norm regularisation may not achieve enough sparsity. The second challenging issue is about the sample imbalance between traffic accidents and normal traffic such that detector would like to favour normal traffic. This study proposes a traffic accident detection method, including a self-tuning iterative hard thresholding (ST-IHT) algorithm for learning sparse spatio-temporal features and a weighted extreme learning machine (W-ELM) for detection. The ST-IHT algorithm can improve the sparsity of encoded features by solving an norm regularisation. The W-ELM can put more focus on traffic accident samples. Meanwhile, a two-point search strategy is proposed to adaptively find a candidate value of Lipschitz coefficients to improve the tuning precision. Experimental results in our collected dataset have shown that this proposed traffic accident detection algorithm outperforms other state-of-the-art methods in terms of the feature's sparsity and detection performance. © The Institution of Engineering and Technology 2019

Keyword:

Community:

  • [ 1 ] [Yu, Y.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 2 ] [Xu, M.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
  • [ 3 ] [Gu, J.]Department of Electrical and Computer Engineering, Dalhousie University, Halifax, NS, Canada

Reprint 's Address:

  • [Yu, Y.]College of Mathematics and Computer Science, Fuzhou UniversityChina

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Source :

IET Intelligent Transport Systems

ISSN: 1751-956X

Year: 2019

Issue: 9

Volume: 13

Page: 1417-1428

2 . 4 8

JCR@2019

2 . 3 0 0

JCR@2023

ESI HC Threshold:150

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 19

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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