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Hybrid Feature-Assisted Neural Model for Crowd Behavior Analysis

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Abstract

The exponential rise in technology and allied applications has always revitalized academia industries to achieve more efficient and robust solution to meet contemporary demands. Surveillance systems have always been the dominant area which has grabbed the attention of the scientific community to enable real-time events or target’s characterization to make timely decision process. Crowd behavior analysis and classification is one of the most sought, though complex system to meet at hand surveillance purposes. However, unlike pedestrian movement detection methods, crowd analysis and behavioral characterization require robust feature learning and classification. With this motive, in this paper, a highly robust model is developed by applying hybrid deep features containing statistical features of the gray-level co-occurrence matrix (GLCM) and transferable deep learning AlexNet high-dimensional features. In addition, to perform multi-class classification multi-feed forward neural network model (MFNN) is used. Here, the inclusion of hybrid features of GLCM and AlexNet provides deep spatio-temporal feature information which helps in making optimal classification decision. On the other hand, the use of MFNN algorithm enables optimal multi-class classification. Thus, the combined model with hybrid deep features and MFNN achieves crowd behavior classification with 91.35% accuracy, 89.92% precision, 88.34% recall and F-measure of 89.12%.

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Acknowledgements

The authors would like to thank the Management, the Principal and the authorities of Malnad College of Engineering, Hassan, for extending full support in carrying out this research work.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan, Visvesvaraya Technological University, Belagavi, Karnataka, India

    H. Y. Swathi

  2. Department of Electronics and Instrumentation Engineering, Malnad College of Engineering, Hassan, Visvesvaraya Technological University, Belagavi, Karnataka, India

    G. Shivakumar

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  1. H. Y. Swathi
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  2. G. Shivakumar
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Correspondence to H. Y. Swathi.

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This article is part of the topical collection “Data Science and Communication” guest-edited by Kamesh Namudri, Naveen Chilamkurti, Sushma S. J. and S. Padmashree.

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Swathi, H.Y., Shivakumar, G. Hybrid Feature-Assisted Neural Model for Crowd Behavior Analysis. SN COMPUT. SCI. 2, 248 (2021). https://doi.org/10.1007/s42979-021-00636-2

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