NDLI: Vision-based traffic accident detection using sparse spatio-temporal features and weighted extreme learning machine

Content Provider	IET Digital Library
Author	Yu, Yuanlong Xu, Miaoxing Gu, Jason
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.
Starting Page	1417
Ending Page	1428
Page Count	12
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 9, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5409
Journal	IET Intelligent Transport Systems
Publisher Date	2019-05-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Challenging Issue Computer Vision Computer Vision And Image Processing Technique Discriminative Spatio-temporal Feature Representations Feature Extraction Hand-craft Feature Image Representation Intelligent Transport System Interpolation And Function Approximation Iterative Method Knowledge Engineering Technique L1-norm Regularisation Learning in AI Lipschitz Coefficient Normal Traffic Numerical Analysis Optical, Image And Video Signal Processing Road Accidents Robust Spatio-temporal Feature Representations Sample-wise Weighting-based Self-tuning Iterative Hard Thresholding Algorithm Sparse Coding Algorithm Sparse Coding Technique Sparse Spatio-temporal Feature ST-IHT Algorithm Statistics Traffic Accident Detection Algorithm Traffic Accident Detection Method Traffic Accident Sample Traffic Accidents Traffic Engineering Computing Vision-based Traffic Accident Detection Weighted Extreme Learning Machine
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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