Background Modeling and Foreground Object Detection for Indoor Video Sequence

被引：0

作者：

Kumar, N. Satish ^{[1
]}

Shobha, G. ^{[1
]}

机构：

[1] RV Coll Engn, CSE Dept, Bangalore, Karnataka, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA ENGINEERING AND COMMUNICATION TECHNOLOGY, ICDECT 2016, VOL 2 | 2017年 / 469卷

关键词：

Background subtraction; Homomorphic filter; Euclidian distance;

D O I：

10.1007/978-981-10-1678-3_77

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposed efficient and reliable algorithm for background subtraction for indoor video sequences. The paper mainly focused on compensating the illumination variation in the frame and then applying improved Gaussian mixture model to build the background model and then detect the moving foreign objects in indoor video sequences by using the Euclidian distance as metric. To compensate the illumination variation, homomorphic filtering algorithm for color image in HSV color space was proposed in the paper. The paper also reported the performance evaluation of the proposed method and existing and found that the proposed algorithm achieved 80 % of improvement in detecting actual foreground objects in indoor video sequences having illumination variations.

引用

下载

页码：799 / 807

页数：9

共 50 条

[21] Joint learning of foreground, background and edge for salient object detection
Wu, Qin
Zhu, Pengcheng
Chai, Zhilei
Guo, Guodong
COMPUTER VISION AND IMAGE UNDERSTANDING, 2024, 240
[22] Video-Based Background/Foreground Estimation for Vehicle Detection
Czapla, Zbigniew
INTELLIGENT SOLUTIONS FOR CITIES AND MOBILITY OF THE FUTURE, 2022, 352 : 110 - 119
[23] Anomaly Foreground Detection through Background Learning in Video Surveillance
Tang, Cheng-Yuan
Wu, Yi-Leh
Chao, Shih-Pin
Chen, Wen-Chao
Chen, Pan-Lan
NEW ADVANCES IN INTELLIGENT DECISION TECHNOLOGIES, 2009, 199 : 427 - 435
[24] On the role and the importance of features for background modeling and foreground detection
Bouwmans, Thierry
Silva, Caroline
Marghes, Cristina
Zitouni, Mohammed Sami
Bhaskar, Harish
Frelicot, Carl
COMPUTER SCIENCE REVIEW, 2018, 28 : 26 - 91
[25] Mixed Gaussian Model Based Video Foreground Object Detection
Zhang, Caixia
Liu, Xiaoxiao
Xu, Qingyang
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE2018), 2018,
[26] Statistical modeling of complex backgrounds for foreground object detection
Li, LY
Huang, WM
Gu, IYH
Tian, Q
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2004, 13 (11) : 1459 - 1472
[27] VERSATILE BAYESIAN CLASSIFIER FOR MOVING OBJECT DETECTION BY NON-PARAMETRIC BACKGROUND-FOREGROUND MODELING
Cuevas, Carlos
Mohedano, Raul
Garcia, Narciso
2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012), 2012, : 313 - 316
[28] Region-based statistical background modeling for foreground object segmentation
De Beek, Kristof Op
Gu, Irene Yu-Hua
Li, Liyuan
Viberg, Mats
De Moor, Bart
2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS, 2006, : 3317 - +
[29] Foreground-Background Distribution Modeling Transformer for Visual Object Tracking
Yang, Dawei
He, Jianfeng
Ma, Yinchao
Yu, Qianjin
Zhang, Tianzhu
2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, : 10083 - 10093
[30] An Efficient Foreground Object Detection Method Using a Color Cluster-Based Background Modeling Algorithm
Tsai, Wen-Kai
Shue, Ming-Hwa
2016 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C), 2016, : 736 - 739

← 1 2 3 4 5 →