{"title":"Research on tracking of moving objects based on depth feature detection","authors":"Guocai Zuo, Xiaoli Zhang, Jing Zheng","doi":"10.1504/ijcse.2023.133691","DOIUrl":null,"url":null,"abstract":"In complex conditions such as illumination change, target rotation, and background clutter, tracking drift or target failure tracking may occur. Convolutional neural networks (CNN) can achieve robust target tracking in complex scenes such as illumination, rotation, background clutter, and so on. Therefore, this paper proposes a target tracking algorithm CNNT based on a convolutional neural network. Use CNN deep learning model to extract the deep features of the sample to complete the target detection task, and then use the kernel correlation filter (KCF) target tracking algorithm to complete the target tracking. We train the Visual Geometry Group (VGG), deep learning model using massive image data, extract the depth feature of tracking targets through the trained VGG deep learning model, and use the depth feature for target detection. The results of the experiment show that, compared with other algorithms such as KCF, the CNNT algorithm achieves a more robust target tracking effect in complex scenes such as illumination change, target rotation, and background clutter.","PeriodicalId":47380,"journal":{"name":"International Journal of Computational Science and Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":1.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computational Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijcse.2023.133691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In complex conditions such as illumination change, target rotation, and background clutter, tracking drift or target failure tracking may occur. Convolutional neural networks (CNN) can achieve robust target tracking in complex scenes such as illumination, rotation, background clutter, and so on. Therefore, this paper proposes a target tracking algorithm CNNT based on a convolutional neural network. Use CNN deep learning model to extract the deep features of the sample to complete the target detection task, and then use the kernel correlation filter (KCF) target tracking algorithm to complete the target tracking. We train the Visual Geometry Group (VGG), deep learning model using massive image data, extract the depth feature of tracking targets through the trained VGG deep learning model, and use the depth feature for target detection. The results of the experiment show that, compared with other algorithms such as KCF, the CNNT algorithm achieves a more robust target tracking effect in complex scenes such as illumination change, target rotation, and background clutter.
期刊介绍:
Computational science and engineering is an emerging and promising discipline in shaping future research and development activities in both academia and industry, in fields ranging from engineering, science, finance, and economics, to arts and humanities. New challenges arise in the modelling of complex systems, sophisticated algorithms, advanced scientific and engineering computing and associated (multidisciplinary) problem-solving environments. Because the solution of large and complex problems must cope with tight timing schedules, powerful algorithms and computational techniques, are inevitable. IJCSE addresses the state of the art of all aspects of computational science and engineering with emphasis on computational methods and techniques for science and engineering applications.