Int. Symp. On Artificial Intelligence and Signal Processing , 2015-03-03

Title : ( Counterattack Detection in Broadcast Soccer Videos using Camera Motion Estimation )

Authors: Sigari Mohammad Hossein , Soltanian-Zadeh Hamid , vahid kiani , Hamid Reza Pourreza ,

Citation: BibTeX | EndNote

Abstract

This paper presents a new method for counterattack detection using estimated camera motion and evaluates some classification methods to detect this event. To this end, video is partitioned to shots and view type of each shot is recognized first. Then, relative pan of the camera during far-view and mediumview shots is estimated. After weighting of pan value of each frame according to the type of shots, the video is partitioned to motion segments. Then, motion segments are refined to achieve better results. Finally, the features extracted from consecutive motion segments are investigated for counterattack detection. We propose two methods for counterattack detection: (1) rule-based (heuristic rules) and (2) SVM-based. Experiments show that the SVM classifier with linear or RBF kernel results in the best results.

Keywords

, Broadcast soccer video, counterattack detection, camera motion estimation, event detection, video analysis
برای دانلود از شناسه و رمز عبور پرتال پویا استفاده کنید.

@inproceedings{paperid:1052828,
author = {Sigari Mohammad Hossein and Soltanian-Zadeh Hamid and Kiani, Vahid and Pourreza, Hamid Reza},
title = {Counterattack Detection in Broadcast Soccer Videos using Camera Motion Estimation},
booktitle = {Int. Symp. On Artificial Intelligence and Signal Processing},
year = {2015},
location = {مشهد, IRAN},
keywords = {Broadcast soccer video; counterattack detection; camera motion estimation; event detection; video analysis},
}

[Download]

%0 Conference Proceedings
%T Counterattack Detection in Broadcast Soccer Videos using Camera Motion Estimation
%A Sigari Mohammad Hossein
%A Soltanian-Zadeh Hamid
%A Kiani, Vahid
%A Pourreza, Hamid Reza
%J Int. Symp. On Artificial Intelligence and Signal Processing
%D 2015

[Download]