Abstract: Human action recognition is an important issue in the field of computer vision.Existing action recognition methods mostly belong to supervised learning category,in which a large number of labeled data are needed to train the recognition model.However,in many real-world tasks,labeled data are often expensive to get,while unlabeled data are readily available in abundance.In this paper,a novel human action recognition algorithm,named as Co-KNN-SVM,was proposed based on hybrid collaborative training.Different types of recognition methods for action recognition field are employed in this method to build the base classifiers,which are then iteratively retrained to increase their generalization abilities.In general,our method can decrease the labeling cost and achieve complementary advantages of different recognition algorithms.In order to decrease the impact of the noise in pseudo labeled data and improved the recognition performance,we also improved the selection method for the pseudo label data and the iterative training strategy in co-trai-ning style algorithms.The experimental results show that the proposed algorithms can identify human action in the vi-deo more effectively.

Key words: Action recognition,Supervised learning,Hybrid collaborative training,Noise