Background-modelling techniques for foreground detection and Tracking using Gaussian Mixture Model

背景减法 计算机科学 人工智能 卡尔曼滤波器 计算机视觉 混合模型 前景检测 跟踪(教育) 帧(网络) 目标检测 特征(语言学) 特征提取 视频跟踪 模式识别(心理学) 对象(语法) 像素 心理学 教育学 电信 语言学 哲学
作者
R Meghana,Yojan Chitkara,S M Apoorva,Mohana
标识
DOI:10.1109/iccmc.2019.8819825
摘要

Background Modelling and Foreground detection in sports has been achieved by cleverly developing a model of a background from a video by deducing knowledge from frames and comparing this model to every subsequent frame and subtracting the background region from it, hence leaving the foreground detected. This output from GMM background subtraction is fed into the feature extraction algorithm, which segregates the players based on teams. By extracting information of primary colors from each frame, the design of the algorithm based on the color of preference is done. Tracking algorithms Kalman and extended Kalman Filters help to predict and correct the location of players and in correctly estimating their trajectory on the field. Challenges such as shadowing, occlusions and illumination changes are addressed. The designed algorithms are tested against a set of performance parameters for the following datasets (Norway and FIFA) using MATLAB (2017b) and the inferences are respectively made. Object detection, motion detection and Kalman filter algorithms are implemented and the observed results are 100%, 84% and 100% accuracy respectively. With the results quantification and performance analysis, it is observed that with the decrease in contrast between player jerseys a decrease in detection accuracy occurs and with players crowded regions on the field and occluded players a decrease in tracking accuracy was observed.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
3秒前
爱笑晓曼发布了新的文献求助20
6秒前
老大蒂亚戈应助YJ888采纳,获得10
7秒前
JamesPei应助潇湘雪月采纳,获得10
7秒前
bbczj发布了新的文献求助10
9秒前
10秒前
11秒前
南风知我意完成签到,获得积分20
12秒前
段一帆发布了新的文献求助30
14秒前
wangqinlei完成签到 ,获得积分10
14秒前
fenghp发布了新的文献求助10
15秒前
王馨雨发布了新的文献求助10
15秒前
17秒前
CipherSage应助ccalvintan采纳,获得10
18秒前
18秒前
雪天的阳完成签到 ,获得积分10
20秒前
21秒前
22秒前
22秒前
烟花应助ren采纳,获得10
23秒前
讨厌科研发布了新的文献求助10
23秒前
量子星尘发布了新的文献求助10
24秒前
苏卿应助科研通管家采纳,获得30
25秒前
fd163c应助科研通管家采纳,获得10
26秒前
香蕉觅云应助科研通管家采纳,获得10
26秒前
思源应助科研通管家采纳,获得10
26秒前
SYLH应助科研通管家采纳,获得10
26秒前
26秒前
CAOHOU应助科研通管家采纳,获得10
26秒前
爆米花应助科研通管家采纳,获得10
26秒前
SYLH应助科研通管家采纳,获得30
26秒前
小蘑菇应助科研通管家采纳,获得10
26秒前
26秒前
殷勤的紫槐完成签到,获得积分10
26秒前
风轻青柠发布了新的文献求助10
27秒前
27秒前
机智冬灵完成签到,获得积分10
28秒前
29秒前
为小嗳打伞完成签到 ,获得积分10
31秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989390
求助须知:如何正确求助?哪些是违规求助? 3531487
关于积分的说明 11254109
捐赠科研通 3270153
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809174