A novel random forests based class incremental learning method for activity recognition

Chunyu Hu, Yiqiang Chen, Lisha Hu, Xiaohui Peng

Abstract: Highlights • A novel separating axis theorem (SAT) based splitting strategy is proposed. • By combining SAT based splitting strategy and traditional splitting strategy, we propose a novel class incremental random forests algorithm (CIRF). • Performance of CIRF on three public activity recognition datasets is competitive and robust. Abstract Automatic activity recognition is an active research topic which aims to identify human activities automatically. A significant challenge is to recognize new activities effectively. In this paper, we propose an effective class incremental learning method, named Class Incremental Random Forests (CIRF), to enable existing activity recognition models to identify new activities. We design a separating axis theorem based splitting strategy to insert internal nodes and adopt Gini index or information gain to split leaves of the decision tree in the random forests (RF). With these two strategies, both inserting new nodes and splitting leaves are allowed in the incremental learning phase. We evaluate our method on three UCI public activity datasets and compare with other state-of-the-art methods. Experimental results show that the proposed incremental learning method converges to the performance of batch learning methods (RF and extremely randomized trees). Compared with other state-of-the-art methods, it is able to recognize new class data continuously with a better performance.