 |
    |
Continuous Learning of Human Activity Models Using Deep Nets***
Mahmudul Hasan and Amit K. Roy-Chowdhury
University of California, Riverside, USAAbstract. Learning activity models continuously from streaming videos is an immensely important problem in video surveillance, video indexing, etc. Most of the research on human activity recognition has mainly focused on learning a static model considering that all the training instances are labeled and present in advance, while in streaming videos new instances continuously arrive and are not labeled. In this work, we propose a continuous human activity learning framework from streaming videos by intricately tying together deep networks and active learning. This allows us to automatically select the most suitable features and to take the advantage of incoming unlabeled instances to improve the existing model incrementally. Given the segmented activities from streaming videos, we learn features in an unsupervised manner using deep networks and use active learning to reduce the amount of manual labeling of classes. We conduct rigorous experiments on four challenging human activity datasets to demonstrate the effectiveness of our framework for learning human activity models continuously.
Keywords: Continuous Learning, Active Learning, Deep Learning, Action Recognition
Electronic Supplementary Material:
*This work was supported in part by ONR grant N00014-12-1-1026 and NSF grant IIS-1316934. Mahmudul Hasan is with Dept. of Computer Science and Amit K. Roy-Chowdhury is with Dept. of Electrical Engineering at UCR.
LNCS 8691, p. 705 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014