Design of Adaptive Target Tracking Algorithm for Robots Based on Visual Attention Mechanism

Abstract

Adaptive target tracking with visual attention represents a sophisticated approach to object detection and localization in dynamic environments. With principles inspired by human visual perception, this methodology employs mechanisms of selective attention to prioritize relevant visual information for tracking moving targets. By dynamically adjusting attentional focus based on salient visual cues and target motion characteristics, adaptive target tracking enhances the efficiency and accuracy of object localization in cluttered scenes. This research presents a novel adaptive target tracking algorithm designed for robotic systems, integrating a visual attention mechanism with the Fuzzy Clustering Multi-Point Tracking utilizing the Green Channel (FC-MPT-GC) approach. The proposed FC-MPT-GC model comprises of Fuzzy Clustering for the extraction of features in the robots-based environment. The FC-MPT-GC model uses the estimation of green channels in the classification environment. With the estimation of features in the environment with Fuzzy C-means clustering green channels are deployed in the deep learning, The proposed algorithm aims to enhance the adaptability and precision of target tracking in dynamic environments. By incorporating a visual attention mechanism, the algorithm dynamically allocates attentional focus to salient regions of the visual input, optimizing the tracking process for moving targets. The FC-MPT-GC methodology further refines target localization by utilizing fuzzy clustering and multi-point tracking strategies, particularly leveraging information from the Green Channel to improve robustness in various lighting conditions. Simulation analysis demonstrated that the proposed FC-MPT-GC model tracking accuracy is achieved at 95.1% with the minimal computation time of 15.2 ms.