A combined approach of Kullback–Leibler divergence and background subtraction for moving object detection in thermal video

Abstract In this work, a robust method for moving object detection in thermal video frames has been proposed by including Kullback–Leibler divergence (KLD) based threshold and background subtraction (BGS) technique. A trimmed-mean based background model has been developed that is capable enough to reduce noise or dynamic component of the background. This work assumed that each pixel has normally distributed. The KLD has computed between background pixel and a current pixel with the help of Gaussian mixture model. The proposed threshold is useful enough to classify the state of each pixel. The post-processing step uses morphological tool for edge linking, and then the flood-fill algorithm has applied for hole-filling, and finally the silhouette of targeted object has generated. The proposed methods run faster and have validated over various real-time based problematic thermal video sequences. In the experimental results, the average value of F 1 -score, area under the curve, the percentage of correct classification, Matthew’s correlation coefficient show higher values whereas total error and percentage of the wrong classification show minimum values. Moreover, the proposed-1 method achieved higher accuracy and execution speed with minimum false alarm rate that has been compared with proposed-2 as well as considered peer methods in the real-time thermal video.

[1]  Hong Zhou,et al.  A novel background subtraction method based on color invariants , 2013, Comput. Vis. Image Underst..

[2]  M. A. Siegler,et al.  Automatic Segmentation, Classification and Clustering of Broadcast News Audio , 1997 .

[3]  Dar-Shyang Lee,et al.  Effective Gaussian mixture learning for video background subtraction , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Debasis Chaudhuri,et al.  Frequency and Spatial Domains Adaptive-based Enhancement Technique for Thermal Infrared Images , 2014 .

[5]  Wanying Xu,et al.  A novel infrared small moving target detection method based on tracking interest points under complicated background , 2014 .

[6]  Amitava Das,et al.  Adaptive contour-based statistical background subtraction method for moving target detection in infrared video sequences , 2014 .

[7]  Guillaume-Alexandre Bilodeau,et al.  SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity , 2015, IEEE Transactions on Image Processing.

[8]  Bitao Fu,et al.  Moving Object Detection Based on the Histograms of Oriented Gradients and Cloud Model , 2012 .

[9]  Sangwook Lee,et al.  Low-complexity background subtraction based on spatial similarity , 2014, EURASIP J. Image Video Process..

[10]  Sateesh K. Peddoju,et al.  Classification and comparison of NoSQL big data models , 2015, Int. J. Big Data Intell..

[11]  Banshidhar Majhi,et al.  Intensity Range Based Background Subtraction for Effective Object Detection , 2013, IEEE Signal Processing Letters.

[12]  Cláudio Rosito Jung,et al.  Efficient Background Subtraction and Shadow Removal for Monochromatic Video Sequences , 2009, IEEE Transactions on Multimedia.

[13]  Manoranjan Paul,et al.  On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[14]  Vladimir S. Petrovic,et al.  Multisensor background extraction and updating for moving target detection , 2008, 2008 11th International Conference on Information Fusion.

[15]  Ripul Ghosh,et al.  Moving target detection in thermal infrared imagery using spatiotemporal information. , 2013, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  Dileep Kumar Yadav,et al.  Moving object detection in real-time visual surveillance using background subtraction technique , 2014, 2014 14th International Conference on Hybrid Intelligent Systems.

[17]  Tao Xiang,et al.  Background Subtraction with DirichletProcess Mixture Models , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Fatih Murat Porikli,et al.  Changedetection.net: A new change detection benchmark dataset , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[19]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[20]  Ajith Abraham,et al.  Toward a lightweight framework for monitoring public clouds , 2012, 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN).

[21]  Jordi Gonzàlez,et al.  Combining where and what in change detection for unsupervised foreground learning in surveillance , 2015, Pattern Recognit..

[22]  Srdan T. Mitrovic,et al.  Suboptimal threshold estimation for detection of point-like objects in radar images , 2015, EURASIP J. Image Video Process..

[23]  Xiaowei Zhou,et al.  Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Johnny S. Wong,et al.  A Brief Review on Leading Big Data Models , 2014, Data Sci. J..

[25]  Winston H. Hsu,et al.  Real-time privacy-preserving moving object detection in the cloud , 2013, ACM Multimedia.

[26]  Shun-ichi Amari,et al.  The AIC Criterion and Symmetrizing the Kullback–Leibler Divergence , 2007, IEEE Transactions on Neural Networks.

[27]  Brian C. Lovell,et al.  Improved Foreground Detection via Block-Based Classifier Cascade With Probabilistic Decision Integration , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  Sugam Sharma,et al.  Evolution of as-a-Service Era in Cloud , 2015, ArXiv.

[29]  Srikanta Patnaik,et al.  Leading NoSQL models for handling Big Data: a brief review , 2016, Int. J. Bus. Inf. Syst..

[30]  Ajith Abraham,et al.  Secure Private Cloud Architecture for Mobile Infrastructure as a Service , 2012, 2012 IEEE Eighth World Congress on Services.