Name
Abstract | ||
|---|---|---|
This paper discusses the task of continuous human action recognition. By continuous, it refers to videos that contain multiple actions which are connected together. This task is important to applications like video surveillance and content based video retrieval. It aims to identify the action category and detect the start and end key frame of each action. It is a challenging task due to the frequent changes of human actions and the ambiguity of action boundaries. In this paper, a novel and efficient continuous action recognition framework is proposed. Our approach is based on the bag of words representation. A visual local pattern is regarded as a word and the action is modeled by the distribution of words. A generative translation and scale invariant probabilistic Latent Semantic Analysis model is presented. The continuous action recognition result is obtained frame by frame and updated from time to time. Experimental results show that this approach is effective and efficient to recognize both isolated actions and continuous actions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s11042-012-1084-2 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Continuous human action analysis,Probabilistic Latent Semantic Analysis model,Real time system,Start and end key frame detection | Bag-of-words model,Computer vision,Video retrieval,Computer science,Action recognition,Speech recognition,Real-time operating system,Probabilistic latent semantic analysis,Artificial intelligence,Generative grammar,Key frame,Ambiguity | Journal |
Volume | Issue | ISSN |
68 | 3 | 1380-7501 |
Citations | PageRank | References |
9 | 0.50 | 30 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Guo | 1 | 9 | 0.50 |
| Zhenjiang Miao | 2 | 356 | 58.01 |
| Yuan Shen | 3 | 1151 | 111.52 |
| Wanru Xu | 4 | 47 | 14.23 |
| Dianyong Zhang | 5 | 20 | 1.73 |