Journal of Smart Science and Technology PENERBIT PRESS UNIVERSITI TEKNOLOGI MARA 2023 March Volume 3 Issue 1 eISSN: 2785-924X 63 Performance of Correlational Filtering and Deep Learning Based Single Target Tracking Algorithms ZhongMing Liao 1,2,* , Azlan Ismail 1,3 1 School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia 2 XinYu College, JiangXi 338004, P.R.China 3 Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Al-Khawarizmi Complex, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia Received: 05-09-2022 Revised: 24-12-2022 Accepted: 10-01-2023 Published: 30-03-2023 *Correspondence Email: liaozhongming168@gmail.com (ZhongMing Liao) DOI: https://doi.org/10.24191/jsst.v3i1.42 © 2023 The Author(s). Published by UiTM Press. This is an open access article under the terms of the Creative Commons Attribution 4.0 International Licence (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Abstract Visual target tracking is an important research element in the field of computer vision. The applications are very wide. In terms of the computer vision field, deep learning has achieved remarkable results. It has broken through many complex problems that are difficult to be solved by traditional algorithms. Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. This paper closely follows the tracking framework of target tracking algorithms and discusses in detail the traditional visual target tracking methods, the mainstream single target tracking algorithms based on correlation filtering, and the video single target tracking algorithms based on deep learning. Experiments were conducted on OTB100 and VOT2018 benchmark datasets, and the experimental data obtained were analysed to derive two visual single-target tracking algorithms with optimal tracking performance. Finally, the future development of tracking algorithms is envisioned. Keywords Deep learning; Correlation filtering; Target tracking algorithms Citation: Liao, Z., & Ismail, A. (2023). Performance of correlational filtering and deep learning based single target tracking algorithms. Journal of Smart Science and Technology, 3(1), 63-79. 1 Introduction Visual target tracking is a fundamental and important research topic in the field of computer vision, which has received a great deal of attention from scholars. Given the state (position and size) of a target in the first frame of a video, the aim is to predict the state of the target in subsequent frames 1,2 . Visual target tracking has wide and deep applications in human-computer interaction, intelligent video surveillance, medical diagnosis, visual navigation, and other fields. Although visual target tracking technology has been studied for many years and some progresses have been made, it is still difficult to meet the practical needs, such as scale change, fast motion, deformation, blur, illumination change, occlusion, and background clutter in some situations. Many academics attempt to improve target tracking and overcome its problems 3,4 , mainly including the challenging factors like the self-factor and background factors as shown in Figure 1. Often, multiple challenges are faced in a tracking task, which makes it particularly important to design a robust tracking algorithm that can cope with a variety of complex situations.