Research on tracking of moving objects based on depth feature detection

IF 1.4 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Computational Science and Engineering Pub Date : 2023-01-01 DOI:10.1504/ijcse.2023.133691
Guocai Zuo, Xiaoli Zhang, Jing Zheng
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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.
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基于深度特征检测的运动目标跟踪研究
在光照变化、目标旋转、背景杂波等复杂条件下,可能出现跟踪漂移或目标跟踪失败。卷积神经网络(CNN)可以在光照、旋转、背景杂波等复杂场景下实现鲁棒目标跟踪。为此,本文提出了一种基于卷积神经网络的目标跟踪算法CNNT。使用CNN深度学习模型提取样本的深度特征完成目标检测任务,然后使用核相关滤波器(KCF)目标跟踪算法完成目标跟踪。我们利用海量图像数据训练视觉几何组(Visual Geometry Group, VGG)深度学习模型,通过训练好的VGG深度学习模型提取跟踪目标的深度特征,并利用深度特征进行目标检测。实验结果表明,与KCF等其他算法相比,CNNT算法在光照变化、目标旋转、背景杂波等复杂场景下实现了更加鲁棒的目标跟踪效果。
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来源期刊
International Journal of Computational Science and Engineering
International Journal of Computational Science and Engineering COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
40.00%
发文量
73
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.
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