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.


Mechanical and Aerospace Engineering

Keywords and Phrases

HMM; Basketball Video; Feature Extraction; Filtering Theory; Hidden Markov Models; Human Satisfaction Rate; Image Classification; Image Recognition; Image Representation; Image Sequences; Motion Based Event Recognition; Motion Estimation; Motion Filters; Motion Patterns; Motion Representation Scheme; Semantic Video Analysis; Sport; Video Signal Processing; Video Understanding

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2002 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2002

Included in

Chemistry Commons