Open Access

ARTICLE

Multiple-Object Tracking Using Histogram Stamp Extraction in CCTV Environments

by Ye-Yeon Kang¹, Geon Park¹, Hyun Yoo², Kyungyong Chung^1,*

1 Division of AI Computer Science and Engineering, Kyonggi University, Suwon, 16227, Korea
2 Contents Convergence Software Research Institute, Kyonggi University, Suwon, 16227, Korea

* Corresponding Author: Kyungyong Chung. Email:

Computers, Materials & Continua 2023, 77(3), 3619-3635. https://doi.org/10.32604/cmc.2023.043566

Received 06 July 2023; Accepted 14 November 2023; Issue published 26 December 2023

Abstract

Object tracking, an important technology in the field of image processing and computer vision, is used to continuously track a specific object or person in an image. This technology may be effective in identifying the same person within one image, but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same. When tracking the same object using two or more images, there must be a way to determine that objects existing in different images are the same object. Therefore, this paper attempts to determine the same object present in different images using color information among the unique information of the object. Thus, this study proposes a multiple-object-tracking method using histogram stamp extraction in closed-circuit television applications. The proposed method determines the presence or absence of a target object in an image by comparing the similarity between the image containing the target object and other images. To this end, a unique color value of the target object is extracted based on its color distribution in the image using three methods: mean, mode, and interquartile range. The Top-N accuracy method is used to analyze the accuracy of each method, and the results show that the mean method had an accuracy of 93.5% (Top-2). Furthermore, the positive prediction value experimental results show that the accuracy of the mean method was 65.7%. As a result of the analysis, it is possible to detect and track the same object present in different images using the unique color of the object. Through the results, it is possible to track the same object that can minimize manpower without using personal information when detecting objects in different images. In the last response speed experiment, it was shown that when the mean was used, the color extraction of the object was possible in real time with 0.016954 s. Through this, it is possible to detect and track the same object in real time when using the proposed method.

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Keywords

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