Resource Optimisation using Multithreading in Support Vector Machine

Image processing is one of the most important features for vision-based robotic and being used in various applications to increase productivity. Various researchers reported issues computation problem to detect objects in low cost device such as vision-based robotic car. In the fast-paced development of technology, a system that runs automatically with the right results is essential to the completion of a job. This study aims to propose an effective multithreading for road sign recognition. We implemented multithreading algorithm for train and detector processes in SVM to utilise the multicore CPU and evaluate in various condition on by a Raspberry Pi platform. It aims to solve the real-time computation issue using Pi camera. Experimental results show significant improvement of performance to the detection accuracy. In conclusion multithreading significantly improve the detection performance using Raspberry Pi processors with various image resolution and number of SVM model.