Multi-object tracking: a systematic literature review

• Published in: Multimedia tools and applications Vol. 83; no. 14; pp. 43439 - 43492
• Main Authors: Hassan, Saif, Mujtaba, Ghulam, Rajput, Asif, Fatima, Noureen
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2024; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Literature reviews; Multimedia Information Systems; Multiple target tracking; Object recognition; Special Purpose and Application-Based Systems; Systematic review; Track 6: Computer Vision for Multimedia Applications; Deep learning; Multi-tracking; Computer vision; Re-identification augmentation; Multi-Object tracking
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-17297-3

The field of computer vision is revolutionized with the advancement of deep learning and the availability of high computational power. In addition, in the field of computer vision, object detection, and tracking have gained much interest. Several authors are proposing new approaches to detect and track multiple objects from a given video frame and publishing their novel approaches in well-reputed academic journals and proceedings. However, a comprehensive systematic literature review is needed to summarize and critically appraise the existing primary works on multi-object tracking approaches. Therefore, to address this, we aim to produce a systematic literature review on multi-object tracking approaches published from 2019 to 2021. In addition, in this paper, we have systematically reviewed different datasets, multi object-tracking approaches, and performances of existing approaches. Further, we have also presented the critical appraisal of existing primary studies on the subject matter. Finally, we have also provided seven future research directions for future researchers who are interested in further contributing to the field of multi-object tracking. We believe that this work will be beneficial for novice as well as expert researchers who are further willing to contribute to the field of multi-object tracking.