Context-aware framework for analysis of horse-race videos
type: CreativeWork
description: Today sports analysis systems draw the attention of many commercial entities and are providing many opportunities for computer vision researchers to study and develop automated sports analysis systems. The primary objective of a sports video analysis is to interpret low-level visual features to high-level semantics that can be understood by the end user. Although all sports video analysis systems are based on similar principles at the low-level visual extraction, interpreting this visual information to high-level human understanding is extremely specific to the sport in question. This project is the very first attempt to evaluate the jockeys’ performance from horse-race TV broadcasts. The aim of this thesis is to extract high-level information from the horse race by automatically detecting and tracking jockeys, specifically at turning points of the race. However, detecting and tracking of jockeys are extremely difficult. This is because in most horse-races there are more than six jockeys and they closely follow each other to gain a leading position, consequently locating jockeys and maintaining their identities through a video sequence is highly challenging due to the frequent occlusion. In addition, the background in horse-race videos is continually changing, thus there is not only background clutter but the jockeys themselves may be obscured by obstacles such as trees, towers and bars. To tackle these challenges, a context-aware analysis system is proposed. The proposed system is developed based on deterministic reasoning which was obtained by observing various horse races. One important property of horse-races is the group dynamic behaviour of jockeys in the race. The jockeys race around a circular track and the camera typically follow the jockeys, thus the jockeys relative to each other tend to move as a slowly changing group. This homogeneous characteristic of jockeys in the race is very useful, especially when local information of jockeys is poor or abrupt due…
abstract: Today sports analysis systems draw the attention of many commercial entities and are providing many opportunities for computer vision researchers to study and develop automated sports analysis systems. The primary objective of a sports video analysis is to interpret low-level visual features to high-level semantics that can be understood by the end user. Although all sports video analysis systems are based on similar principles at the low-level visual extraction, interpreting this visual information to high-level human understanding is extremely specific to the sport in question. This project is the very first attempt to evaluate the jockeys’ performance from horse-race TV broadcasts. The aim of this thesis is to extract high-level information from the horse race by automatically detecting and tracking jockeys, specifically at turning points of the race. However, detecting and tracking of jockeys are extremely difficult. This is because in most horse-races there are more than six jockeys and they closely follow each other to gain a leading position, consequently locating jockeys and maintaining their identities through a video sequence is highly challenging due to the frequent occlusion. In addition, the background in horse-race videos is continually changing, thus there is not only background clutter but the jockeys themselves may be obscured by obstacles such as trees, towers and bars. To tackle these challenges, a context-aware analysis system is proposed. The proposed system is developed based on deterministic reasoning which was obtained by observing various horse races. One important property of horse-races is the group dynamic behaviour of jockeys in the race. The jockeys race around a circular track and the camera typically follow the jockeys, thus the jockeys relative to each other tend to move as a slowly changing group. This homogeneous characteristic of jockeys in the race is very useful, especially when local information of jockeys is poor or abrupt due…
author: Hedayati, Mohammad
keywords: sports analysis systems, object tracking, data associations, Context-Aware Framework
license: https://researchcommons.waikato.ac.nz/bitstreams/5543fa4e-5921-4a13-bf31-a04de50d9cce/download
date published: 2018
date modified: 2026-07-24T05:57:27Z
provider: University Waikato
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Waikato