The advance algorithms like Faster Regional Convolutional Neural Network (Faster R-CNN) models are suitable to identify classified moving objects, due to the efficiency in learning the training dataset superior than ordinary CNN algorithms and the higher accuracy of labeling correct classes in the validation and testing dataset. This research examined and compared the three R-CNN type algorithms in object recognition to show the superior efficiency and accuracy of Faster R-CNN model on classifying human running patterns. Then it described the effect of Faster R-CNN in detecting different types of running patterns exhibited by a single individual or multiple individuals by conducting a dataset fitting experiment. In this study, the Faster R-CNN algorithm is implemented directly from the version released by Ross Girshick.
翻译:由于学习比普通CNN算法优越的培训数据集的效率以及验证和测试数据集中正确等级标签的准确性较高,例如快速区域革命神经网络(Aster R-CNN)模型等先进算法适合于识别分类移动物体,这项研究对目标识别中的三种R-CNN型算法进行了检查和比较,以显示快速R-CNN模型在人类运行模式分类方面的超高效率和准确性。随后,它描述了快速R-CNN模型在通过进行数据组合匹配试验来检测个人或多人展示的不同类型运行模式方面的效果。在这项研究中,快速R-CNN算法直接从罗斯·吉希希克发布的版本中实施。