基于深度学习和智能规划的行为识别

Action Recognition Based on Deep Learning and Artificial Intelligence Planning

现有行为识别方法在未能持续覆盖造成视频监控盲区所引起行为数据缺失的情况,难以有效实施特征分析、行为分类补全,无法准确识别出智能体完整的行为动作序列.为此,本文提出一种基于深度学习和智能规划的行为识别方法.首先,利用深度残差网络对图像进行分类训练,然后使用递归神经网络对图像特征进行提取深度信息以增强分类效果;其次,运用智能规划的STRIPS(Stanford Research Institute Problem Solver)模型,将深度学习提取的图像特征命题信息转化为规划领域的模型描述文档,并使用前向状态空间搜索规划器推导出完整的行为动作序列.在HMDB51等行为识别公共数据集中,本方法与生成式对抗网络、深度卷积逆向图网络、深度信念网络、支持向量机等同类先进方法相比展现出更好的性能.

Currently,action recognition methods can hardly carry out feature analysis,behavior classification,and action completion,and are incapable of accurately identifying the complete behavioral action sequence of intelligent agent for the discontinuous and incomplete motion capture,behavioral data missing or even broken in the time dimension,which are resulted from sensor device not being continuous coverage caused by the monitoring blind area.In this regard,we put forward a method of action recognition based on deep learning and artificial intelligence planning.Firstly,a deep learning network is constructed,by which the image is classified and trained using DRN(Deep Residual Network).After that,the extraction depth information of image frame feature for recurrent neural network is trained to enhance the classification effect.Secondly,the STRIPS(Stanford Research Institute Problem Solver)planning model is used to extract the image feature of deep learning,transforming into the description document for domain model,which facilitates deriving the optimal planning solution by means of forward state-space search planner.In the experiment,we exhibit that our method outperforms baselines in the public datasets,e.g.,DCIGN(Deep Convolutional Inverse Graphics Networks),GAN(Generative Adversarial Networks),DBN(Deep Belief Networks),and SVM(Support Vector Machine).