Name
Title | ||
|---|---|---|
Recognizing Human Actions By Bp-Adaboost Algorithm Under A Hierarchical Recognition Framework |
Abstract | ||
|---|---|---|
This paper explores the performance of Neural Network (NN) for human action recognition and proposes a novel hierarchical and boosting-based action recognition system. Specifically, the main contributions of our work are three-fold: (1) A boosted NN based scheme is applied to the human action recognition task for the first time, during which we extend the standard binary AdaBoost algorithm to a multiclass version; (2) A novel hierarchical recognition framework with pre-decision and post-decision modules is proposed, which can significantly enhance the training efficiency as well as the frame-based recognition accuracy; (3) Numerous modified features (both motion and shape features) are utilized and combined in this paper. Experiments on the Weizmann dataset show promising results of our approach in comparison with other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638290 | 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
action recognition, feature extraction, BP-AdaBoost, neural network, pre/post-decision | 3D single-object recognition,Pattern recognition,Computer science,Feature extraction,Time delay neural network,Feature (machine learning),Artificial intelligence,Backpropagation,Artificial neural network,Machine learning,Binary number,Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | ISSN |
null | null | 1520-6149 |
Citations | PageRank | References |
5 | 0.41 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nijun Li | 1 | 37 | 4.59 |
| Xu Cheng | 2 | 43 | 7.36 |
| Suofei Zhang | 3 | 34 | 7.26 |
| Zhenyang Wu | 4 | 5 | 0.41 |