Abstract
Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous work on activity recognition focuses on recognition from video clips that show only single activities. There are few published algorithms for segmenting and recognizing complex activities that are composed of more than one single activity. In this paper, we present a novel HMM-based approach that uses threshold and voting to automatically and effectively segment and recognize complex activities. Experiments on a database of video clips of different activities show that our method is effective.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Multimedia and Expo, ICME 2005 |
| Pages | 804-807 |
| Number of pages | 4 |
| Volume | 2005 |
| DOIs | |
| State | Published - Dec 1 2005 |
| Event | IEEE International Conference on Multimedia and Expo, ICME 2005 - Amsterdam, Netherlands Duration: Jul 6 2005 → Jul 8 2005 |
Other
| Other | IEEE International Conference on Multimedia and Expo, ICME 2005 |
|---|---|
| Country | Netherlands |
| City | Amsterdam |
| Period | 7/6/05 → 7/8/05 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
HMM-based segmentation and recognition of human activities from video sequences. / Niu, Feng; Abdel-Mottaleb, Mohamed.
IEEE International Conference on Multimedia and Expo, ICME 2005. Vol. 2005 2005. p. 804-807 1521545.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - HMM-based segmentation and recognition of human activities from video sequences
AU - Niu, Feng
AU - Abdel-Mottaleb, Mohamed
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous work on activity recognition focuses on recognition from video clips that show only single activities. There are few published algorithms for segmenting and recognizing complex activities that are composed of more than one single activity. In this paper, we present a novel HMM-based approach that uses threshold and voting to automatically and effectively segment and recognize complex activities. Experiments on a database of video clips of different activities show that our method is effective.
AB - Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous work on activity recognition focuses on recognition from video clips that show only single activities. There are few published algorithms for segmenting and recognizing complex activities that are composed of more than one single activity. In this paper, we present a novel HMM-based approach that uses threshold and voting to automatically and effectively segment and recognize complex activities. Experiments on a database of video clips of different activities show that our method is effective.
UR - http://www.scopus.com/inward/record.url?scp=33750537020&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33750537020&partnerID=8YFLogxK
U2 - 10.1109/ICME.2005.1521545
DO - 10.1109/ICME.2005.1521545
M3 - Conference contribution
SN - 0780393325
SN - 9780780393325
VL - 2005
SP - 804
EP - 807
BT - IEEE International Conference on Multimedia and Expo, ICME 2005
ER -