Multi-camera multi-object tracking: A review of current trends and future advances

Amosa, T.I. and Sebastian, P. and Izhar, L.I. and Ibrahim, O. and Ayinla, L.S. and Bahashwan, A.A. and Bala, A. and Samaila, Y.A. (2023) Multi-camera multi-object tracking: A review of current trends and future advances. Neurocomputing, 552. ISSN 09252312

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The nascent applicability of multi-camera tracking (MCT) in numerous real-world applications makes it a significant computer vision problem. While visual tracking of objects, especially in video obtained from single camera setup, has drawn huge research attention, the constant identification and tracking of targets as they transit across multiple cameras remains an open research problem. In addition to the linking of target appearance and trajectory information across frames, effective association of such data across multiple cameras is also very critical in MCT. Occlusion, appearance variability, camera motion, as well as nonrigid object structure and motion are widely recognized constraints and major sources of concerns in MCT. In recent years, several literatures have been contributed suggesting a variety of approaches to addressing various problems in MCT. However, studies that critically review and report the advances and trends of research in MCT are still limited. This current study presents a comprehensive and up-to-date review of visual object tracking in multi-camera settings. In this paper, we analyze and categorize existing works based on six crucial facets: problem formulation, adopted problem solving approach, data association requirements, mutual exclusion constraints, benchmark datasets, and performance metrics. Furthermore, the study summarizes the outcomes of 30 state-of-the-art MCT algorithms on common datasets to allow quantitative comparison and analysis of their experimental results. Finally, we examine recent advances in MCT and suggest some promising future research directions. © 2023 The Author(s)

Item Type: Article
Impact Factor: cited By 0
Uncontrolled Keywords: Benchmarking; Security systems, 'current; Intelligent video surveillance; Multi-camera tracking; Multi-cameras; Multi-object tracking; Multicamera systems; Multiple cameras; Real-world; Video surveillance; Visual Tracking, Computer vision, accuracy; algorithm; benchmarking; convolutional neural network; deep learning; eye tracking; geometry; long short term memory network; Markov chain Monte Carlo method; mathematical analysis; motion; Multiple Hypothesis Tracking; probability; quantitative analysis; reaction optimization; Short Survey; trend study; velocity
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 04 Oct 2023 08:41
Last Modified: 04 Oct 2023 08:41

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