A robust scene recognition system for baseball broadcast using data-driven approach

Authors:
Ryoichi Ando;Koichi Shinoda;Sadaoki Furui;Takahiro Mochizuki
Affiliations:
Institute of Technology, Tokyo, Japan;Institute of Technology, Tokyo, Japan;Institute of Technology, Tokyo, Japan;NHK Science & Technical Research Laboratories, Tokyo, Japan
Venue:
Proceedings of the 6th ACM international conference on Image and video retrieval
Year:
2007

Citing 7
Cited 4

Event Detection and Summarization in Sports Video

CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Maximum entropy model-based baseball highlight detection and classification

Computer Vision and Image Understanding - Special issue on event detection in video
Sports video categorizing method using camera motion parameters

ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Robust scene recognition using language models for scene contexts

MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Robust Scene Extraction Using Multi-Stream HMMs for Baseball Broadcast

IEICE - Transactions on Information and Systems
Event based indexing of broadcasted sports video by intermodalcollaboration

IEEE Transactions on Multimedia
Content analysis of video using principal components

IEEE Transactions on Circuits and Systems for Video Technology

Spatio-temporal pyramid matching for sports videos

MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Automatic score scene detection for baseball video

LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
Automatic Pitch Type Recognition System from Single-View Video Sequences of Baseball Broadcast Videos

International Journal of Multimedia Data Engineering & Management
A spatio-temporal pyramid matching for video retrieval

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a robust scene recognition system for baseball broadcast videos. This system is based on the data-driven approach which has been successful in continuous speech recognition. It uses a multi-stream hidden Markov model to model each scene and an unsupervised adaptation method to achieve robustness against differences in environmental conditions among games. It also employs an n-gram language model to represent the contexts among scenes, and a model for scene length information. The proposed system was evaluated in scene recognition experiments with 16 scene types acquired from video data of 25 baseball games. The system reduced errors in scene recognition by 6.3 % absolute.