Research on Optimization of Visual Object Tracking Algorithm Based on Deep Learning

Abstract

The appearance of deep gaining knowledge has extensively advanced the sphere of visible item tracking, permitting greater robust and accurate monitoring of items throughout complex scenes. This study optimises a visual item tracking set of rules based on the Siamese region inspiration network (Siamese RPN) monitoring algorithm, aiming to beautify its efficiency and effectiveness in actual-time packages. The Siamese RPN algorithm, acknowledged for its stability among accuracy and velocity because of its architecture that mixes the Siamese network for characteristic extraction with a vicinity suggestion community for item localisation, provides a promising basis for improvement. This examination introduces numerous optimisations to the authentic Siamese RPN framework. First, we endorse an improved feature extraction model that leverages a more efficient deep neural community structure, lowering computational load while preserving excessive accuracy. 2nd, we optimise the place concept mechanism by incorporating an adaptive anchor scaling method that dynamically adjusts the scale and ratio of anchors based on the object’s scale variations, enhancing the tracking accuracy across distinctive object sizes and aspect ratios. Moreover, we introduce a unique training method that employs an aggregate of actual global and synthetically generated statistics to beautify the robustness of the monitoring algorithm towards various demanding situations, including occlusions, speedy moves, and illumination adjustments. The effectiveness of the proposed optimizations is evaluated through complete experiments on numerous benchmark datasets, consisting of OTB, VOT, and LaSOT, demonstrating extensive upgrades in tracking accuracy and speed as compared to the authentic Siamese RPN algorithm and different modern-day tracking techniques. The outcomes of this study no longer underscore the potential of optimised Siamese RPN algorithms in visible item monitoring but additionally lay the basis for future explorations into actual-time, green, and strong tracking systems. Those improvements keep great promise for a wide variety of packages, from surveillance and protection to autonomous cars and augmented truth structures, where particular and dependable item monitoring is paramount.