Learning general model for activity recognition with limited labelled data

作者：

Highlights：

• We demonstrate that different people perform activities differently.

• Combine AdaBoost with LDA to build general activity model with minimum labelled data.

• Hybrid AdaBoost with HMM&CRF for temporal regulatization of human activities.

• Use publicly available datasets to validate the proposed methods.

摘要

•We demonstrate that different people perform activities differently.•Combine AdaBoost with LDA to build general activity model with minimum labelled data.•Hybrid AdaBoost with HMM&CRF for temporal regulatization of human activities.•Use publicly available datasets to validate the proposed methods.

论文关键词：Activity recognition,General model,Co-training

论文评审过程：Received 21 July 2015, Revised 2 January 2017, Accepted 3 January 2017, Available online 6 January 2017, Version of Record 10 January 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.01.002