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Hybrid tracking model and GSLM based neural network for crowd behavior recognition

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Abstract

Crowd behaviors analysis is the ‘state of art’ research topic in the field of computer vision which provides applications in video surveillance to crowd safety, event detection, security, etc. Literature presents some of the works related to crowd behavior detection and analysis. In crowd behavior detection, varying density of crowds and motion patterns appears to be complex occlusions for the researchers. This work presents a novel crowd behavior detection system to improve these restrictions. The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network. The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network. GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network. The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy. The experimentation results in the crowd behavior detection with a maximum accuracy of 93% which proves the efficacy of the proposed system in video surveillance with security concerns.

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Authors and Affiliations

  1. Department of Computer Engineering and Applications, GLA University, Mathura, India

    Kumar Manoj & Bhatnagar Charul

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  1. Kumar Manoj
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  2. Bhatnagar Charul
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Correspondence to Kumar Manoj.

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Manoj, K., Charul, B. Hybrid tracking model and GSLM based neural network for crowd behavior recognition. J. Cent. South Univ. 24, 2071–2081 (2017). https://doi.org/10.1007/s11771-017-3616-4

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  • DOI: https://doi.org/10.1007/s11771-017-3616-4

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