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Intelligent video analysis for enhanced pedestrian detection by hybrid metaheuristic approach

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Abstract

Intelligent video analytics for pedestrian detection plays a vital role for enhanced and effective surveillance system. Since smart city projects are gaining momentum in most of the countries nowadays, enhanced pedestrian detection plays a vital role in the field of security and surveillance. Various classification models were in existence for detecting the pedestrians which suffers from variety of challenges like illumination, pedestrian outfits, gestures, occlusion, lighting, etc., that affects the accuracy of detection. A strong feature vector describing the pedestrian is developed to enhance the accuracy of detection. In this paper, a novel hybrid metaheuristic pedestrian detection (HMPD) approach is proposed to enhance the accuracy of the classifier. HMPD extracts the working principles of support vector machine and genetic algorithm. The proposed model is trained using a set of human and non-human images. The accuracy of the proposed model is tested with benchmarking video data available at VISOR repository. The result clearly shows that HMPD approach produces the maximum accuracy than any traditional approaches. HMPD approach can further be applied in other domains for enhanced security and surveillance.

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Authors and Affiliations

  1. Department of CSE, KPR Institute of Engineering and Technology, Coimbatore, India

    K. R. Sri Preethaa

  2. Department of IT, K S Rangasamy College of Technology, Tiruchengode, India

    A. Sabari

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  1. K. R. Sri Preethaa
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  2. A. Sabari
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Correspondence to K. R. Sri Preethaa.

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Communicated by V. Loia.

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Sri Preethaa, K.R., Sabari, A. Intelligent video analysis for enhanced pedestrian detection by hybrid metaheuristic approach. Soft Comput 24, 12303–12311 (2020). https://doi.org/10.1007/s00500-020-04674-5

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