Motion based event recognition using HMM

Author

Xu, Gu ; Ma, Yu-Fei ; Zhang, Hong-Jiang ; Yang, Shiqiang

Author_Institution

Dept. of Comput. Sci., Tsinghua Univ., Beijing, China

Volume

2

fYear

2002

fDate

2002

Firstpage

831

Abstract

Motion is an important cue for video understanding and is widely used in many semantic video analyses. We present a new motion representation scheme in which motion in a video is represented by the responses of frames to a set of motion filters. Each of these filters is designed to be most responsive to a type of dominant motion. Then we employ hidden Markov models (HMMs) to characterize the motion patterns based on these features and thus classify basketball video into 16 events. The evaluation by human satisfaction rate to classification result is 75%, demonstrating effectiveness of the proposed approach to recognizing semantic events in video.

Keywords

feature extraction; filtering theory; hidden Markov models; image classification; image recognition; image representation; image sequences; motion estimation; sport; video signal processing; HMM; basketball video; hidden Markov models; human satisfaction rate; motion based event recognition; motion filters; motion patterns; motion representation scheme; semantic video analysis; video understanding; Content based retrieval; Data mining; Event detection; Feature extraction; Filters; Games; Hidden Markov models; Image retrieval; Robustness; Video sequences;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2002. Proceedings. 16th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-1695-X

Type

conf

DOI

10.1109/ICPR.2002.1048431

Filename

1048431