Title
Incremental Action Recognition Using Feature-Tree
Abstract
Action recognition methods suffer from many drawbacks in practice, which include (1)the inability to cope with incremental recognition problems; (2)the requirement of an intensive training stage to obtain good performance; (3) the inability to recognize simultaneous multiple actions; and (4) difficulty in performing recognition frame by frame. In order to overcome all these drawbacks using a single method, we propose a novel framework involving the feature-tree to index large scale motion features using Sphere/Rectangle-tree (SR-tree). The recognition consists of the following two steps: 1) recognizing the local features by non-parametric nearest neighbor (NN), 2) using a simple voting strategy to label the action. The proposed method can provide the localization of the action. Since our method does not require feature quantization, the feature-tree can be efficiently grown by adding features from new training examples of actions or categories. Our method provides an effective way for practical incremental action recognition. Furthermore, it can handle large scale datasets due to the fact that the SR-tree is a disk-based data structure. We have tested our approach on two publicly available datasets, the KTH and the IXMAS multi-view datasets, and obtained promising results. ©2009 IEE.
Publication Date
12-1-2009
Publication Title
Proceedings of the IEEE International Conference on Computer Vision
Number of Pages
1010-1017
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ICCV.2009.5459374
Copyright Status
Unknown
Socpus ID
77953224502 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/77953224502
STARS Citation
Reddy, Kishore K.; Liu, Jingen; and Shah, Mubarak, "Incremental Action Recognition Using Feature-Tree" (2009). Scopus Export 2000s. 11379.
https://stars.library.ucf.edu/scopus2000/11379