التفاصيل البيبلوغرافية
| العنوان: |
Enhancing Navigation and Object Recognition for Visually Impaired Individuals: A Gradient Support Vector Boosting-based Crossover Golden Jackal Algorithm Approach. |
| المؤلفون: |
Abidi, Mustufa Haider, Alkhalefah, Hisham, Siddiquee, Arshad Noor |
| المصدر: |
Journal of Disability Research; 2024, Vol. 3 Issue 5, p1-11, 11p |
| مصطلحات موضوعية: |
OBJECT recognition (Computer vision), RECOGNITION (Psychology), PEOPLE with visual disabilities, GUIDE dogs, VISION disorders, SUPPORT vector machines |
| مستخلص: |
On a global scale, individuals with vision impairments encounter various limitations when it comes to moving around and finding their way independently. Their daily activities are impeded by their limited understanding of their environment while moving about both indoors and outside, where situations are constantly changing. Recent technological breakthroughs have made it possible to create several electronic devices that help visually impaired and disabled people with navigation. These devices encompass navigation systems, obstacle avoidance systems, object localization devices, and orientation assistance systems. They are designed to enhance or substitute conventional aids like guide dogs and white canes. This research work proposes a solution based on the gradient support vector boosting-based crossover golden jackal (GSB-CGJ) algorithm, which integrates various assistive technologies focused on navigation and object recognition, providing intelligent feedback to the user. The developed model focuses on guiding visually impaired individuals, preventing unwanted collisions with obstacles, and generating active feedback. The proposed method consists of three distinct phases. In the input phase, images are acquired from the Image and Video Dataset for Visually Impaired using Intel RealSense Camera. The next stage entails object recognition, which is efficiently carried out using the GSB-CGJ algorithm. The hyperparameters of the support vector machine and adaptive boosting methods are optimized using the golden jackal optimization method, enhancing object recognition ability. At the end, the output phase delivers feedback to the user. The experimental and assessment results validate that the model demonstrates high accuracy in recognizing objects and precision in localizing them. This approach effectively delivers remarkable real-time implementation capability, showcasing better adaptability and reliability while reducing execution time. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
