东南大学学报（自然科学版）2017，Vol.47Issue(4)：691-696,6.DOI:10.3969/j.issn.1001-0505.2017.04.011
基于光流约束自编码器的动作识别
Action recognition based on optical flow constrained auto-encoder
摘要
Abstract
To improve the capability of feature learning in extracting motion information such as amplitudes and directions and to increase the recognition accuracy, an optical flow constrained auto-encoder is proposed to learn action features.The optical flow constrained auto-encoder is an unsupervised feature learning algorithm based on single layer regularized auto-encoder.The algorithm uses the neural network to reconstruct the video pixels and use the corresponding optical flows in video blocks as a revised regularization.The neural network learns the appearances of the action and encodes the motion information simultaneously.The associated codes are used as the final action features.The experimental results on several well-known benchmark datasets show that the optical flow constrained auto-encoder can detect the motion parts efficiently.On the same recognition framework, the proposed algorithm outperforms the state-of-the-art single layer action feature learning algorithms.关键词
动作识别/特征学习/正则化自编码器/光流约束自编码器Key words
action recognition/feature learning/regularized auto-encoder/optical flow constrained auto-encoder分类
信息技术与安全科学引用本文复制引用
李亚玮,金立左,孙长银,崔桐..基于光流约束自编码器的动作识别[J].东南大学学报（自然科学版）,2017,47(4):691-696,6.基金项目
国家自然科学基金资助项目 (61402426). （61402426）
