For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Efficient Class-Incremental Learning Based on Bag-of-Sequencelets Model for Activity Recognition
Jong-Woo LEE Ki-Sang HONG
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2019/09/01
Online ISSN: 1745-1337
Type of Manuscript: PAPER
activity recognition, action classification, class-incremental learning, video classification,
Full Text: PDF>>
We propose a class-incremental learning framework for human activity recognition based on the Bag-of-Sequencelets model (BoS). The framework updates learned models efficiently without having to relearn them when training data of new classes are added. In this framework, all types of features including hand-crafted features and Convolutional Neural Networks (CNNs) based features and combinations of those features can be used as features for videos. Compared with the original BoS, the new framework can reduce the learning time greatly with little loss of classification accuracy.