Name
Title | ||
|---|---|---|
Saliency-based foreground trajectory extraction using multiscale hybrid masks for action recognition. |
Abstract | ||
|---|---|---|
Action recognition in realistic scenes is a challenging task in the field of computer vision. Although trajectory-based methods have demonstrated promising performance, background trajectories cannot be filtered out effectively, which leads to a reduction in the ratio of valid trajectories. To address this issue, we propose a saliency-based sampling strategy named foreground trajectories on multiscale hybrid masks (HM-FTs). First, the motion boundary images of each frame are calculated to derive the initial masks. According to the characteristics of action videos, image priors and the synchronous updating mechanism based on cellular automata are exploited to generate an optimized weak saliency map, which will be integrated with a strong saliency map obtained via the multiple kernels boosting algorithm. Then, multiscale hybrid masks are achieved through the collaborative optimization strategy and masks intersection. The compensation schemes are designed to extract a set of foreground trajectories that are closely related to human actions. Finally, a hybrid fusion framework for combining trajectory features and pose features is constructed to enhance the recognition performance. The experimental results on two benchmark datasets demonstrate that the proposed method is effective and improves upon most of the state-of-the-art algorithms. (c) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1117/1.JEI.27.5.053049 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
action recognition,foreground trajectory,saliency detection,multiscale hybrid masks,features fusion | Computer vision,Cellular automaton,Collaborative optimization,Pattern recognition,Computer science,Salience (neuroscience),Action recognition,Boosting (machine learning),Sampling (statistics),Artificial intelligence,Prior probability,Trajectory | Journal |
Volume | Issue | ISSN |
27 | 5 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoliang Zhang | 1 | 1 | 3.05 |
| Songmin Jia | 2 | 103 | 28.69 |
| Xiangyin Zhang | 3 | 0 | 2.03 |
| Xiuzhi Li | 4 | 21 | 11.40 |