Vision-Based Detection and Tracking System in Robotics Using Visual Servoing and Deep Learning Algorithms

Abstract

Robot vision, a vital intersection of robotics and computer vision, enables machines to interpret their environment and respond intelligently. This study presents a vision-based detection and tracking system utilizing visual servoing principles integrated with deep learning algorithms. The implementation features a Raspberry Pi-based control unit interfaced with a camera module and pan-tilt servos to dynamically track targets in real-time. Haar Cascade, CSRT-DCF, and multiple advanced tracking algorithms were evaluated for performance and robustness in varying conditions. Emphasis is laid on real-time tracking accuracy, visual feedback response, and control coordination between image sensors and actuators. OpenCV libraries were used to implement object detection, face recognition, and movement prediction, enhancing the reliability of the visual servoing system. The design offers potential applications in service robotics, autonomous navigation, and real-time surveillance. Simulation results demonstrate consistent performance across diverse scenarios, affirming the system’s capability to adapt and maintain target tracking despite occlusions and lighting variations. This work signifies a step forward in the integration of AI-powered computer vision within robotic systems for autonomous and intelligent decision-making.