Title: Research on tracking of moving objects based on depth feature detection

Authors: Guocai Zuo; Xiaoli Zhang; Jing Zheng

Addresses: Hunan Software Vocational and Technical University, Kaiyuan Road, Jiuhua Economic Development Zone, Xiangtan, Hunan, China ' Hunan Polytechnic of Water Resources and Electric Power, Changsha, Hunan Province, China ' Lang Fang Normal University, Aimin West Road 100#, Anci District, Langfang City, Hebei Province, China

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.

Keywords: target tracking; deep learning; convolutional neural network; CNN.

DOI: 10.1504/IJCSE.2023.133691

International Journal of Computational Science and Engineering, 2023 Vol.26 No.5, pp.591 - 601

Received: 05 Aug 2022
Received in revised form: 19 Dec 2022
Accepted: 28 Dec 2022
Published online: 29 Sep 2023 *

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