Name
Abstract | ||
|---|---|---|
In this paper, we propose a human action recognition system suitable for embedded computer vision applications in security systems, human-computer interaction and intelligent environments. Our system is suitable for embedded computer vision application based on three reasons. Firstly, the system was based on a linear Support Vector Machine (SVM) classifier where classification progress can be implemented easily and quickly in embedded hardware. Secondly, we use compacted motion features easily obtained from videos. We address the limitations of the well known Motion History Image (MHI) and propose a new Hierarchical Motion History Histogram (HMHH) feature to represent the motion information. HMHH not only provides rich motion information, but also remains computationally inexpensive. Finally, we combine MHI and HMHH together and extract a low dimension feature vector to be used in the SVM classifiers. Experimental results show that our system achieves significant improvement on the recognition performance. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CVPR.2007.383420 | 2007 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-8 |
Keywords | Field | DocType |
embedded systems,gesture recognition,support vector machine,machine intelligence,human computer interaction,hardware,feature extraction,application software,feature vector,image classification,computer vision,computer security,embedded computing,support vector machines,history | Histogram,Computer vision,Feature vector,Pattern recognition,Computer science,Support vector machine,Gesture recognition,Feature extraction,Artificial intelligence,Application software,Classifier (linguistics),Contextual image classification | Conference |
Volume | Issue | ISSN |
2007 | 1 | 1063-6919 |
Citations | PageRank | References |
39 | 2.85 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongying Meng | 1 | 832 | 69.39 |
| Nick Pears | 2 | 410 | 30.57 |
| Chris Bailey | 3 | 78 | 8.32 |