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Optimal feed forward neural network based automatic moving vehicle detection system in traffic surveillance system

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Abstract

In intelligent transportation system, traffic surveillance is an important topic. One challenging problem for complex urban traffic surveillance system is robust vehicle detection and tracking. Therefore, in this paper, we develop a two-stage approach for moving vehicle detection system. The proposed system mainly consists of two stages such as hypothesis generation (HG) and hypothesis verification (HV). In the first step, we generate the hypotheses using shadows under vehicles is darker than road region concept. In the second step, we verify hypotheses generated in the first step whether correct or not using optimal feedforward neural network (OFFNN). Here, to extract vehicle features, we utilize two types of histogram orientation gradients descriptors (HOG). In training stage, the histogram orientation gradients features are given to the OFFNN classifier. The weights corresponding FFNN is optimally select using improved grasshopper optimization algorithm (IGOA). The experimental results show that the proposed moving vehicle detection system performs better accuracy compare to other methods.

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Authors and Affiliations

  1. AMC Engineering College, Bangalore, 560083, India

    J. A. Smitha

  2. Hindusthan College of Engineering and Technology, Coimbatore, 641032, India

    N. Rajkumar

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  1. J. A. Smitha
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  2. N. Rajkumar
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Correspondence to J. A. Smitha.

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Smitha, J.A., Rajkumar, N. Optimal feed forward neural network based automatic moving vehicle detection system in traffic surveillance system. Multimed Tools Appl 79, 18591–18610 (2020). https://doi.org/10.1007/s11042-020-08757-1

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  • DOI: https://doi.org/10.1007/s11042-020-08757-1

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