Computer Science ›› 2021, Vol. 48 ›› Issue (7): 233-237.doi: 10.11896/jsjkx.200600131

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Moving Object Detection Based on Region Extraction and Improved LBP Features

XIN Yuan-xue, SHI Peng-fei, XUE Rui-yang   

  1. College of Internet of Things Engineering,Hohai University,Changzhou,Jiangsu 213022,China
  • Received:2020-06-22 Revised:2020-08-30 Online:2021-07-15 Published:2021-07-02
  • About author:XIN Yuan-xue,born in 1987,Ph.D,lecturer.Her main research interests include communication system, information acquisition and processing.
  • Supported by:
    National Natural Science Foundation of China(61801168, 61801169) and Jiangsu Province Natural Science Foundation(BK20170305).

Abstract: Detection accuracy of moving object is dramatically affected by the dynamic natural background,for instance,the shaking leaves and varying illumination.Therefore,it is essential to distinguish between the dynamic background and the foreground moving object.The existing foreground extraction algorithm extracts the foreground point by point,which leads to a waste of computing resources.This paper proposed a novel moving object detection algorithm based on region extraction and improved Local Binary Patterns (LBP).First,the image is divided into several image blocks of same size,and the Kernel Density Estimation (KDE) model is established according to the statistical characteristics of these image blocks.The foreground region is estimated by the KDE model.Then,the improved LBP texture feature histogram of all pixels in the foreground block is obtained.By ma-tching the histogram,all the foreground pixels are extracted,and the background is updated with a probabilistic model.The experimental results show that the proposed method can quickly extract the foreground region of moving target and eliminate most of the interference caused by dynamic background.Compared with the traditional algorithm,the proposed method is more suitable for moving object detection in natural scenes.

Key words: Dynamic background, Kernel density estimation, Local binary patterns texture features, Moving object detection, Region extraction

CLC Number: 

  • TP391.41
[1]JAVED S,MAHMOOD A,ALMAADEED S,et al.Moving Object Detection in Complex Scene Using Spatiotemporal Structured-Sparse RPCA[J].IEEE Transactions on Image Proces-sing,2019,28(2):1007-1022.
[2]REZAEI B,OSTADABBAS S.Moving Object DetectionThrough Robust Matrix Completion Augmented With Objectness[J].IEEE Journal of Selected Topics in Signal Processing,2018,12(6):1313-1323.
[3]ELTANTAWY A,SHEHATA M S.An Accelerated Sequential PCP-Based Method for Ground-Moving Objects Detection From Aerial Videos[J].IEEE Transactions on Image Processing,2019,28(12):5991-6006.
[4]ZHENG P,BAI H Y,LI Z M,et al.Design of accurate detection and tracking algorithm for moving target under jitterinterfe-rence[J].Chinese Journal of Scientific Instrument,2019,40(11):90-98.
[5]HAO X L,LIU W,NIU B N,et al.High-Efficiency Detection Algorithm for Moving Targets Based on Adaptive Learning Rate[J].Journal of University of Electronic Science and Technology of China,2020,49(1):123-130.
[6]SENGAR S S,MUKHOPADHYAY S. Moving object area detection using normalized self-adaptive optical flow[J].Journal for Light and Electronoptic,2016,127(16):6258-6267.
[7]XI Y,JIA K,SUN Z,et al.A Moving Object Detection Algorithm Based on a Combination Optical Flow and Edge Detection[C]//Intelligent Data Analysis.2016:130-137.
[8]GUO C,ZHANG L.A Novel Multiresolution SpatiotemporalSaliency Detection Model and Its Applications in Image and Vi-deo Compression[J].IEEE Transactions on Image Proces-sing,2010,19(1):185-198.
[9]GUO Y,LI Z,LIU Y,et al.Video Object Extraction Based on Spatiotemporal Consistency Saliency Detection[J].IEEE Access,2018,6:35171-35181.
[10]ABBASIFARD M R,NADERI H,ALAMDARI O I,et al.Efficient Indexing For Past and Current Position of Moving Objects on Road Networks[J].IEEE Transactions on Intelligent Transportation Systems,2018,19(9):2789-2800.
[11]LU J,WANG Z,ZHU J.Space-time multiscale based moving object detection method[J].Journal of Northwestern Polytechnical University,2017,35(1):98-102.
[12]PANDO A G,MURGUIA M I.Analysis and Trends on Moving Object Detection Algorithm Techniques[J].IEEE Latin America Transactions,2019,17(11):1771-1783.
[13]ELHARROUSS O,MOUJAHID D,TAIRI H.Moving objectdetection with an adaptive background model[C]//Intelligent Systems and Computer Vision.2017.
[14]ROMERO J D,LADO M J,MENDEZ A J,et al.A Background Modeling and Foreground Detection Algorithm Using Scaling Coefficients Defined With a Color Model Called Lightness-Red-Green-Blue[J].IEEE Transactions on Image Processing,2018,27(3):1243-1258.
[15]STAUFFER C,GRIMSON W E.Adaptive background mixture models for real-time tracking[C]//Computer Vision and Pattern Recognition.1999:246-252.
[16]BARNICH O,VAN DROOGENBROECK M.ViBe:A Universal Background Subtraction Algorithm for Video Sequences[J].IEEE Transactions on Image Processing,2011,20(6):1709-1724.
[17]KRYJAK T,KOMORKIEWICZ M,GORGON M,et al.Real-time implementation of foreground object detection from a mo-ving camera using the ViBe algorithm[J].Computer Science and Information Systems,2014,11(4):1617-1637.
[18]TAO H,LU X.Automatic smoky vehicle detection from traffic surveillance video based on vehicle rear detection and multi-feature fusion[J].IET Intelligent Transport Systems,2019,13(2):252-259.
[19]NIRANJIL K A,SURESHKUMAR C.Background Subtraction in Dynamic Environment based on Modified Adaptive GMM with TTD for Moving Object Detection[J].Journal of Electrical Engineering & Technology,2015,10(1):372-378.
[20]EVANGELIO R H,PATZOLD M,KELLER I,et al.Adaptively Splitted GMM With Feedback Improvement for the Task of Background Subtraction[J].IEEE Transactions on Information Forensics and Security,2014,9(5):863-874.
[21]ELGAMMAL A,HARWOOD D,DAVIS L S,et al.Non-parametric Model for Background Subtraction[C]//European Conference on Computer Vision.2000:751-767.
[22]LIU C,YUEN P C,QIU G,et al.Object motion detection using information theoretic spatio-temporal saliency[J].Pattern Re-cognition,2009,42(11):2897-2906.
[23]HEIKKILA M,PIETIKAINEN M.A texture-based method for modeling the background and detecting moving objects[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):657-662.
[24]DING Y,LI W H,FAN J T,et al.Robust moving object detection under complex background[J].Computer Science and Information Systems,2010,7(1):201-210.
[25]CHAKRABORTI T,CHATTERJEE A.A novel binary adap-tive weight GSA based feature selection for face recognition using local gradient patterns,modified census transform,and local binary patterns[J].Engineering Applications of Artificial Intelligence,2014,33:80-90.
[26]KIM B,CHOI J,JOO S,et al.Errata:Haar-like compact local binary pattern for illumination-robust feature matching[J].Journal of Electronic Imaging,2012,21(4):49801-49801.
[27]PARCA G,TEIXEIRA P,TEIXEIRA A,et al.All-optical image processing and compression based on Haar wavelet transform[J].Applied Optics,2013,52(12):2932-2939.
[28]XUE G,SUN J,SONG L.Dynamic background subtractionbased on spatial extended center-symmetric local binary pattern[C]//IEEE International Conference on Multimedia & Expo.IEEE,2010.
[29]ZHANG E,LI Y,DUAN J.Moving object detection based on confidence factor and cslbp features[J].The Imaging Science Journal,2016,64(5):253-261.
[30]GOYAL K,SINGHAI J.Texture-based self-adaptive movingobject detection technique for complex scenes[J].Computers & Electrical Engineering,2016,70:275-283.
[1] JIN Hua, ZHU Jing-yu, WANG Chang-da. Review on Video Privacy Protection [J]. Computer Science, 2022, 49(1): 306-313.
[2] ZHANG Ye, LI Zhi-hua, WANG Chang-jie. Kernel Density Estimation-based Lightweight IoT Anomaly Traffic Detection Method [J]. Computer Science, 2021, 48(9): 337-344.
[3] LIU Jun-qi, LI Zhi and ZHANG Xue-yang. Candidate Region Detection Method for Maritime Ship Based on Visual Saliency [J]. Computer Science, 2020, 47(6A): 237-241.
[4] ZHU Xuan, WANG Lei, ZHANG Chao, MEI Dong-feng, XUE Jia-ping, CAO Qing-wen. Moving Object Detection Based on Continuous Constraint Background Model Deduction [J]. Computer Science, 2019, 46(6): 311-315.
[5] ZHOU Jian, XU Hai-qin. Image Edge Detection Method Based on Kernel Density Estimation [J]. Computer Science, 2018, 45(6A): 239-241.
[6] DONG Xiao-jun, CHENG Chun-ling. K-CFSFDP Clustering Algorithm Based on Kernel Density Estimation [J]. Computer Science, 2018, 45(11): 244-248.
[7] XU Jian-rui, LI Zhan-wu and XU An. KDE-CGA Algorithm of Structure Learning for Small Sample Data Bayesian Network [J]. Computer Science, 2017, 44(Z11): 437-441.
[8] ZHANG Wen-ya, XU Hua-zhong and LUO Jie. Moving Objects Detection under Complex Background Based on ViBe [J]. Computer Science, 2017, 44(9): 304-307.
[9] REN Dian-yuan, WANG Wen-wei and MA Qiang. Background Subtraction Based on Color and Local Binary Similarity Pattern [J]. Computer Science, 2016, 43(3): 296-300.
[10] YANG Guo-liang,ZHOU Dan and ZHANG Jin-hui. Moving Object Detection Algorithm Using SILTP Texture Information [J]. Computer Science, 2014, 41(4): 302-305.
[11] HU Xiao-ran and SUN Han. Novel Moving Object Detection Method Based on ViBe [J]. Computer Science, 2014, 41(2): 149-152.
[12] TIAN Hong-jin and ZHAN Yin-wei. Moving Object Detection Based on Adaptive Image Blocking and SSIM [J]. Computer Science, 2014, 41(2): 119-122.
[13] JIANG Peng,QIN Na,ZHOU Yan,TANG Peng and JIN Wei-dong. Automatic Motion Segmentation of Sparse Feature Points with Mean Shift [J]. Computer Science, 2013, 40(8): 273-276.
[14] ZHANG Kun,WANG Cui-rong and WAN Cong. Adaptive Threshold Background Modeling Algorithm Based on Chebyshev Inequality [J]. Computer Science, 2013, 40(4): 287-291.
[15] . Approach of Moving Object Detection Based on Image Blocks and Improved Particle Filter Algorithm [J]. Computer Science, 2012, 39(11): 261-263.
Full text



No Suggested Reading articles found!