Optimizing a Novel Tracking System for Living Beings and Objects through Advanced Mathematical Modeling and Graph Theory
DOI:
https://doi.org/10.56294/dm2024.406Keywords:
Tracking System, Tracking, Graph Learning, HypergraphAbstract
This study extends the formulation of a tracking system for both live items and living persons, and gives a thorough theoretical framework for an advanced tracking system. A large number of tracking systems in use today were created inside certain frameworks and designed to monitor in either infinite or restricted spatial contexts. The latter typically makes use of specialized technological instruments designed with tracking objects or living things in mind. Our contribution to this topic is the formulation of a system theory that both formulates and innovates the challenge of monitoring objects and living things. Graphical modeling is widely used in tracking, which is interesting because it makes it easier to create precise relationships between the objects that need to be tracked and other parts of the system. But our study argues that the best way to achieve a high-performing, contextually relevant, and flexible system in a range of scenarios is still to build a tracking system around graphs, both theoretically and practically. We provide a unique tracking method to further the discipline, based on the ideas of hypergraphs and graph learning. This method carefully examines the order between various linkages inside the system, allowing the system to fully use both direct and indirect relations. The way we formulate tracking is as a complex search problem on graphs and hypergraphs. In this case, the system's components—living things or objects—are represented by vertices, and the kinds of relationships that exist between them are indicated by edges. We present a governing law that facilitates different processing tasks, manages shared data across system parts, and defines the connections between vertices. Additionally, we provide illustrated examples covering single and multi-context tracking scenarios to support our work. These illustrations highlight how, in comparison to current tracking technologies, the suggested approach performs better theoretically. In addition to adding to the theoretical conversation, this discovery has potential applicability in a variety of tracking contexts
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