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Adaptive fuzzy weighted color histogram and HOG appearance model for object tracking with a dynamic trained neural network prediction

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Abstract

Object tracking in video sequences represents an important task in computer vision applications like human mobility and social distance in pandemic scenarios, intelligent surveillance systems, etc. This paper presents a new method, fuzzy weighted color histogram neural network (FWCHNN) to estimate the position of an object and to update the appearance object model during tracking. The method involves two stages. Prediction is achieved through an online dynamic trained neural network, and correction is performed with a spiral sliding window. The method employs a new similitude appearance measure based on a codified model of the object considering its shape and color. The shape is represented with HOG features and the color features from information of a fuzzy weighted color histogram. FWCHNN shows the comparable results regarding state-of-the-art methods.

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Authors and Affiliations

  1. Tecnologico Nacional de Mexico/I.T. Chihuahua, 31030, Chihuahua, Chih., Mexico

    Mario I. Chacon-Murguia, Andrea Rivero-Olivas & Juan A. Ramirez-Quintana

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  1. Mario I. Chacon-Murguia
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  2. Andrea Rivero-Olivas
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  3. Juan A. Ramirez-Quintana
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Correspondence to Mario I. Chacon-Murguia.

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Chacon-Murguia, M.I., Rivero-Olivas, A. & Ramirez-Quintana, J.A. Adaptive fuzzy weighted color histogram and HOG appearance model for object tracking with a dynamic trained neural network prediction. SIViP 15, 1585–1592 (2021). https://doi.org/10.1007/s11760-021-01891-9

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  • DOI: https://doi.org/10.1007/s11760-021-01891-9

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