Computer Science ›› 2016, Vol. 43 ›› Issue (6): 294-297.doi: 10.11896/j.issn.1002-137X.2016.06.058

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Vehicle Detection Research Based on Adaptive SILTP Algorithm

LI Fei, ZHANG Xiao-hong, ZHAO Chen-qiu and YAN Meng   

  • Online:2018-12-01 Published:2018-12-01

Abstract: This paper presented an adaptive SILTP algorithm based on the SILTP algorithm to improve the efficiency of vehicle detection in complex background.The vehicle detection starts with a two-dimensional discrete wavelet trans-form for the image.The next steps of vehicle detection include extracting the vehicle images’ information texture with the adaptive SILTP algorithm,using Gauss mixture model for background modeling,and using the texture information of the new image to update background dynamically. Finally,moving vehicle is obtained by comparing with the background model.The results demonstrate that this detection algorithm can achieve high detection efficiency under a complex background ,and has strong adaptability.

Key words: Local texture characteristics,Background modeling,Vehicle detection,Adaptive SILTP algorithm

[1] Liu H,Hou X.Moving Detection Research of Background Frame Difference Based on Gaussian Model[C]∥International Confe-rence on Computer Science and Service System.IEEE,2012:258-261
[2] Yan L,Zhi-jian J,Gao J W,et al.Robot fish detection based on a combination method of three-frame-difference and background subtraction[C]∥Control and Decision Conference,Chinese.IEEE,2014:3905-3909
[3] Hegenbart S,Uhl A.A scale-adaptive extension to methodsbased on LBP using scale-normalized Laplacian of Gaussian extrema in scale-space[C]∥2014 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2014:4319-4323
[4] Huang Y,Ou Z,Hsieh H,et al.A rapid texture-based moving object detection method[C]∥2011 8th Asian Control Confe-rence (ASCC).IEEE,2011:1205-1209
[5] Qin Y,Tang Y.Dynamic texture recognition based on multiple statistical features with LBP/WLD[C]∥2011 International Conference on Computer Science and Network Technology (ICCSNT).IEEE,2011:957-960
[6] Nguyen D T,Ogunbona P,Li W.Human detection with contour-based local motion binary patterns[C]∥2011 18th IEEE International Conference on Image Processing (ICIP).IEEE,2011:3609-3612
[7] Liao S,Zhao G,Kellokumpu V,et al.Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2010:1301-1306
[8] Oualla M,Sadiq A,Mbarki S.A survey of Haar-Like feature representation[C]∥2014 International Conference on Multimedia Computing and Systems (ICMCS).IEEE,2014:1101-1106
[9] Dos Santos V R,Pilla M,Reiser R,et al.Int-Haar:Improving Precision of the Haar Interval Wavelet Extension[C]∥2013 2nd Workshop-School on Theoretical Computer Science (WEIT).IEEE,2013:92-96
[10] Yin H,Yang H,Su H,et al.Dynamic background subtraction based on appearance and motion pattern[C]∥2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).IEEE,2013:1-6
[11] Wang H,Sang N,Yan Y.Real-Time Tracking Combined withObject Segmentation[C]∥2014 22nd International Conference on IEEE Pattern Recognition (ICPR).2014:4098-4103
[12] Deng G,Guo K.Self-adaptive background modeling researchbased on change detection and area training[C]∥2014 IEEE Workshop on Electronics,Computer and Applications.2014:59-62
[13] Yuan G,Gao Y,Xu D,et al.A Moving Objects Detection Me-thod Based on a Combination of Im-proved Local Binary Pattern Texture and Hue[M].Springer Berlin Heidelberg,2011:261-268

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