Application of neural network in object motion tracking in the context of big data

Abstract

In the context of big data, the advancements in computer analytics and computational capability have promoted transformative opportunities. At the forefront of this progress, neural networks have emerged as an essential instrument across diverse domains, catalyzing enhancements in operational efficiency. This paper embarks on an exploration of the applications of neural networks within the realm of object motion prediction and tracking, with the primary objective of improving the efficacy and precision of tracking mechanisms. Amidst the escalating complexities of data proliferation, the imperative for streamlined tracking methodologies has intensified. In response, this study investigates the amalgamation of neural networks with object motion prediction and tracking processes. By using the powerful abilities of neural networks, the research aims to bring a significant change to the field. This change will be seen in improved accuracy and faster tracking efficiency. Combining neural networks with motion prediction and tracking creates a cooperative partnership that develops a new system based on data-driven insights. By merging computational power and advanced algorithms, the paper anticipates a fresh direction for tracking technology.