基于三维卷积神经网络的工作流识别方法

Workflow recognition method based on 3D convolutional neural networks

鉴于传统的依赖于目标物体检测与跟踪的动作识别方法很难适用于复杂的生产制造环境,为了实现有效的工作流识别,从运动物体的检测与分割、视频序列中多视图特征向量的提取及工人生产动作的分类识别3方面入手,提出基于3D卷积神经网络的工作流识别框架。给出计算模型与相应的算法,并进行了系统的对比实验。通过实验发现,该方法比传统的隐Markov方法和其他方法在识别速度上提升了32%,在识别率上也提升了9%。

Owing to the problem that traditional method of action recognition based on object detection and tracking might not be applicable to complex manufacturing environments, to realize workflow recognition effectively, by making research on the moving objects detection and tracking, extracting feature vector from video sequence and classification of actions, a multi-view feature extraction framework based on moving object segmentation based on 3D Convolutional Neural Networks was proposed. The calculation model and the corresponding algorithm along with the systematic comparative experiments were also given. Experiments showed that the proposed method could improve the speed of recognition about 32% and the recognition accuracy about 9% compared with traditional Hidden Markov method and other methods.