JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (2): 63-70.doi: 10.6040/j.issn.1672-3961.0.2016.174

Previous Articles     Next Articles

Object detection algorithm based on Bayesian probability estimation in wavelet domain

LIU Yingxia1, WANG Xichang2, TANG Xiaoli3, CHANG Faliang4   

  1. 1. Shandong Communication and Media College, Jinan 250200, Shandong, China;
    2. Shandong Province Academy of Education Recruitment and Examination, Jinan 250011, Shandong, China;
    3. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, West Harrison 10606, New York, America;
    4. School of Control Science and Engineering, Shandong University, Jinan 250011, Shandong, China
  • Received:2016-05-23 Online:2017-04-20 Published:2016-05-23

Abstract: In order to improve the detection algorithm, Bayesian probability estimation model in wavelet domain was built to get a robust threshold, and the detected object could be obtained with the adaptive threshold. Moving Window-Based Double Haar Wavelet Transform for detected image sequence was finished. Bayesian probobility estimation model based on kernel density function was built for low frequency part, and adaptive threshold could be obtained after training and studying. With the threshold to judge the low frequnency part, the binary image could be got. Six video sequences with one targe and multiple targets outdoor and indoor were employed to evaluate the effectiveness of presented algorithm. Experimental results showed that it could give a better detecting results.

Key words: Bayesian probability estimation, object detection, wavelet domain, dynamic background

CLC Number: 

  • TP181
[1] PANCHAL Payal, PRAJAPATI Gaurav, PAREL Savan, et al. A review on object detection and tracking methods[J]. International Journal for Research in Emerging Science and Technology, 2015, 2(1):7-12.
[2] KALIRAJAN K, SUDHA M. Moving object detection for video surveillance[J]. The Scientific World Journal, 2015, 2015:1-10.
[3] ZHAO T, NEVATIA R, WU B. Segmentation and tracking of multiple humans in crowded environments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 30(7):1198-1211.
[4] HU Weiming, ZHOU Xue, LI Wei, et al. Active contour-based visual tracking by integrating colors, shapes, and motions[J]. IEEE Transactions on Image Processing, 2013, 22(5):1778-1792.
[5] SANKARALINGAM E, MAADI A, MALDAGUE X. Outdoor infrared video surveillance: a novel dynamic technique for the subtraction of a changing background of IR images[J]. Infrared Physics & Technology, 2007, 49(3): 261-265.
[6] MEHDI A, GGHOLAM R. A new real-time target tracking algorithm in image sequences based on wavelet transform[C] //Proceedings of the 14th International CSI Computer Conference.[S.l.] :IEEE, 2009:512-517.
[7] UNAL G, KRIM H, YEZZI A. Fast incorporation of optical flow into active polygons[J]. IEEE Transactions on Image Processing, 2005, 14(6):745-759.
[8] GHOSH A, SUBUDHI B N, GHOSH S. Object detection from videos captured by moving camera by fuzzy edge incorporated markov random field and local histogram matching[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2012, 22(8):1127-1135.
[9] ZHOU S K, CHELLAPPA R, MOGHADDAM B. Visual tracking and recognition using appearance-adaptive models in particle filters[J]. IEEE Transactions on Image Processing, 2004, 13(11):1491-1506.
[10] LATECKI L J, LI Q N, BAI X, et al. Skeletonization using SSM of the distance transform[J]. IEEE Transactions Conference on Image Processing, 2007, 5(9):349-352.
[11] CHEN C T, SU C Y, KAO W C. An enhanced segmentation on vision-based shadow removal for vehicle detection[C] //International Conference on Green Circuits and systems.[S.l.] :IEEE, 2010:679-682.
[12] SHEIKH Yaser, SHAH Mubarak. Bayesian modeling of dynamic scenes for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1778-1792.
[13] HARDAS Adesh, BADE Dattatray, WALI Vibha. Moving object detection using background subtraction shadow removal and post processing[J]. European Journal of Pharmaceutical Sciences, 2000, 12(1):41-50.
[14] SAKHARKAR Sanjay S, KAMBLE S D, KHOBRAGADE A S. Object detection and tracking using particle filtering[J]. International Journal of Computer Trends and Technology, 2015, 22(1):16-19.
[15] 赵宏伟,冯嘉,臧雪柏,等. 一种实用的运动目标检测和跟踪算法[J]. 吉林大学学报(工学版),2009, 39(2):386-390. ZHAO Hongwei, FENG Jia, ZANG Xuebai, et al. Practical moving target detection and tracking algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2009, 39(2):386-390.
[16] SHEIKH Yaser, SHAH Mubarak. Bayesian modeling of dynamic scenes for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11):1778-1792.
[17] HSIA Chih Hsien, CHIANG Jen Shiun, GUO Jingming. Multiple moving objects detection and tracking using discrete wavelet transform[M] //Computer and Information Science. Shanghai:[s.n.] , 2011:297-320.
[18] CHENG F, CHEN Y L. Real time multiple object tracking and identification based on discrete wavelet transform[J]. Pattern Recognition, 2006, 39(6):1126-1139.
[19] SHARMA M, KULKARNI A, PUNTAMBEKAR S. Wavelet based adaptive tracking control for uncertain nonlinear systems with input constraints[J]. Nonlinear Dynamics & Systems Theory, 2012, 12(3):279-287.
[20] WANG Xin. Moving window-based double Haar wavelet transform for image processing[J]. IEEE Transactions on Image Processing, 2006, 15(9):2771-2779.
[21] LAI Qing, LIN Yijie. A novel network intrusion detection system based on Bayesian inference: IDS-BI[J]. Journal of Information & Computational Science, 2015, 12(11):4369-4376.
[22] NGUYEN Vu, PHUNG Dinh, PHAM Duc-Son, et al. Bayesian nonparametric approaches to abnormality detection in video surveillance[J]. Annals of Data Science, 2015, 2(1):21-41.
[23] 王星. 非参数估计[M].北京:中国人民大学出版社,2000.
[1] HUI Kaifa, CHENG Keyang, ZHAN Yongzhao. The video synopsis based on the enhanced ViBe algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 43-48.
[2] QIU Xiaoxin1,2, ZHANG Wenqiang1,2*, QIN Jinxian1,2, DU Zhengyang1,2, ZHANG Defeng1,2. Multi-target real-time tracking method under harsh environment [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(2): 21-27.
[3] QIAO Wei1, WANG Hui-yuan1,2, WU Xiao-juan1, LIU Peng-wei1. Crowd object detection and classification based on a chaotic dynamic model [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(2): 19-23.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!