Abstract
We propose a motion recognition strategy that represents each videoclip by a set of filtered images, each of which corresponds to a frame. Using a filtered-image classifier based on support vector machines, we classify a videoclip by applying majority voting over the predicted labels of its filtered images and, for online classification, we identify the most likely type of action at any moment by applying majority voting over the predicted labels of the filtered images within a sliding window. We also define a classification confidence and the associated threshold in both cases, which enable us to identify the existence of an unknown type of motion and, together with the proposed recognition strategy, make it possible to build a real-time motion recognition system that cannot only make classifications in real-time, but also learn new types of motions and recognize them in the future. The proposed strategy is demonstrated on real datasets.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1064-1075 |
| Number of pages | 12 |
| Journal | Computer Vision and Image Understanding |
| Volume | 113 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 1 2009 |
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Keywords
- Human motion recognition
- Recursive filtering
- Support vector machine
Cite this
Human motion recognition using support vector machines. / Cao, Dongwei; Masoud, Osama T.; Boley, Daniel L; Papanikolopoulos, Nikolaos P.
In: Computer Vision and Image Understanding, Vol. 113, No. 10, 01.10.2009, p. 1064-1075.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Human motion recognition using support vector machines
AU - Cao, Dongwei
AU - Masoud, Osama T.
AU - Boley, Daniel L
AU - Papanikolopoulos, Nikolaos P
PY - 2009/10/1
Y1 - 2009/10/1
N2 - We propose a motion recognition strategy that represents each videoclip by a set of filtered images, each of which corresponds to a frame. Using a filtered-image classifier based on support vector machines, we classify a videoclip by applying majority voting over the predicted labels of its filtered images and, for online classification, we identify the most likely type of action at any moment by applying majority voting over the predicted labels of the filtered images within a sliding window. We also define a classification confidence and the associated threshold in both cases, which enable us to identify the existence of an unknown type of motion and, together with the proposed recognition strategy, make it possible to build a real-time motion recognition system that cannot only make classifications in real-time, but also learn new types of motions and recognize them in the future. The proposed strategy is demonstrated on real datasets.
AB - We propose a motion recognition strategy that represents each videoclip by a set of filtered images, each of which corresponds to a frame. Using a filtered-image classifier based on support vector machines, we classify a videoclip by applying majority voting over the predicted labels of its filtered images and, for online classification, we identify the most likely type of action at any moment by applying majority voting over the predicted labels of the filtered images within a sliding window. We also define a classification confidence and the associated threshold in both cases, which enable us to identify the existence of an unknown type of motion and, together with the proposed recognition strategy, make it possible to build a real-time motion recognition system that cannot only make classifications in real-time, but also learn new types of motions and recognize them in the future. The proposed strategy is demonstrated on real datasets.
KW - Human motion recognition
KW - Recursive filtering
KW - Support vector machine
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UR - http://www.scopus.com/inward/citedby.url?scp=69549088139&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2009.06.002
DO - 10.1016/j.cviu.2009.06.002
M3 - Article
VL - 113
SP - 1064
EP - 1075
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
IS - 10
ER -