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Human object detection: An enhanced black widow optimization algorithm with deep convolution neural network

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Abstract

In visual classification, understanding among humans and objects is one of the main problems. Human object detection struggles to detect the human as well as object during complex interactions among them. In literature, some of the methods are presented to detect the human and object based on coarse spatial information and appearance features but they fail in complex situations. Hence, in this paper, Black Widow Optimization (BWO) based Deep Convolutional Neural Network (DCNN) Learning model is designed to identify the human as well as object from the video frames. The hyper parameters of the DCNN are optimally selected with the help of BWO algorithm. In the proposed methodology, pre-processing is used to enhance the image quality as well as removing noise from the images by using the gaussian filter and background subtraction. The human and objects are detected from the video frames with the help of DCNN, and performances are evaluated. The proposed method is implemented in MATLAB and statistical measurements are considered to evaluate the performance such as accuracy, sensitivity, precision, recall and F_Measure, respectively. The proposed method is compared with the existing methods such as Convolutional Neural Network (CNN), CNN-Emperor Penguin Optimization (EPO) and CNN-Particle Swarm Optimization (PSO), respectively.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, College of Engineering and Technology, Bule Hora University, Bule Hora, Ethiopia

    P. Mukilan & Wogderess Semunigus

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  1. P. Mukilan
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  2. Wogderess Semunigus
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Correspondence to P. Mukilan.

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Mukilan, P., Semunigus, W. Human object detection: An enhanced black widow optimization algorithm with deep convolution neural network. Neural Comput & Applic 33, 15831–15842 (2021). https://doi.org/10.1007/s00521-021-06203-3

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  • DOI: https://doi.org/10.1007/s00521-021-06203-3

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