Beach sports image detection based on heterogeneous multi-processor and convolutional neural network

摘要

Many of the broadcasting requirements, setting beach motion data from a dedicated camera and extracting meaningful information from the data raise important research agendas. Therefore, computer vision and machine learning are essential for the automatic or semiautomatic processing of heterogeneous multiprocessors moving to the beach. The detection accuracy of moving images is low, which takes more time. The proposed system has emerged to identify and classify beach sports services from the deployment of arm-worn gyroscopes for semi-professional beach sportspeople. It is based on off-the-shelf heterogeneous multiprocessors of convolutional neural networks that can distinguish between common service types. This shows the potential of wearable technology for beach sports that can provide accurate motion analysis. Preprocessing is used to remove noisy and irrelevant data. Segmentation is the process of splitting a moving image into components or objects in the image. Classification analysis, and offer heterogeneous multi-processor analyzes the latest detailed performance evaluation based on Convolutional Neural Network system. Therefore, this study will use Heterogeneous Multi-Processor beach sports as raw material image detection technology analysis. In this study, an equal edge detection image detection technique, gradation processing, target acquisition and target recognition to the sports video's actual needs combined to meet the needs of different dynamic image detection.