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Intelligent Video Surveillance Dmitry Kangin Intelligent Video Surveillance Dmitry Kangin Supervisors: Prof. Plamen P. Angelov, PhD, DSc, FIEEE, FIET Prof. Garegin Markarian, PhD, DSc, FIET A thesis presented for the degree of Doctor of Philosophy Data Science Group School of Computing and Communications Lancaster University England February 2016 Abstract In the focus of this thesis are the new and modified algorithms for object detection, recognition and tracking within the context of video analytics. The manual video surveillance has been proven to have low effectiveness and, at the same time, high expense because of the need in manual labour of operators, which are additionally prone to erroneous decisions. Along with increase of the number of surveillance cameras, there is a strong need to push for automatisation of the video analytics. The benefits of this approach can be found both in military and civilian applications. For military applications, it can help in localisation and tracking of objects of interest. For civilian applications, the similar object localisation procedures can make the criminal investigations more effective, extracting the meaningful data from the massive video footage. Recently, the wide accessibility of consumer unmanned aerial vehicles has become a new threat as even the simplest and cheapest airborne vessels can carry some cargo that means they can be upgraded to a serious weapon. Additionally they can be used for spying that imposes a threat to a private life. The autonomous car driving systems are now impossible without applying machine vision methods. The industrial applications require automatic quality control, including non-destructive methods and particularly methods based on the video analysis. All these applications give a strong evidence in a practical need in machine vision algorithms for object detection, tracking and classification and gave a reason for writing this thesis. The contributions to knowledge of the thesis consist of two main parts: video tracking and object detection and recognition, unified by the common idea of its applicability to video analytics problems. The novel algorithms for object detection and tracking, described in this thesis, are unsupervised and have only a small number of parameters. The approach is based on rigid motion segmentation by Bayesian filtering. The Bayesian filter, which was proposed specially for this method and contributes to its novelty, is formulated as a generic approach, and then applied to the video analytics problems. The method is augmented with optional object co- ordinate estimation using plain two-dimensional terrain assumption which gives a basis for the algorithm usage inside larger sensor data fusion models. The proposed approach for object detection and classification is based on the evolving systems concept and the new Typicality-Eccentricity Data Analytics (TEDA) framework. The methods are capable of solving classical problems of data mining: clustering, classification, and regression. The methods are proposed in a domain-independent way and are capable of 1 addressing shift and drift of the data streams. Examples are given for the clustering and classification of the imagery data. For all the developed algorithms, the experiments have shown sustainable results on the testing data. The practical applications of the proposed algorithms are carefully examined and tested. 2 Statement of Originality I, Dmitry Kangin, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. 3 Acknowledgements The author is pleased to thank Denis Kolev and Mikhail Suvorov, discussions with whom, as well the co-operation on the articles and book chapter, have contributed significantly to this thesis. The discussions, article co-operation and various help and assistance of my supervisors, Professor Plamen Angelov and Professor Garik Markarian, helped me enormously. Also I need to praise Professor George Kolev for all discussions and help. Also, many appreciation go to my colleagues in Rinicom, to fellows of the EU FP7 TRAX project for object tracking, in which I am happy to participate, and to my parents, Nikolay and Lyudmila, and my sister Evgenia. 4 Contents Abstract............................................................................................................................. 1 Statement of Originality ................................................................................................... 3 Acknowledgements .......................................................................................................... 4 List of Figures................................................................................................................... 8 List of Tables .................................................................................................................. 10 Acronyms & Abbreviations ............................................................................................ 11 1 Research Overview ................................................................................................ 13 1.1 Motivation .......................................................................................................... 13 1.2 Research Contribution ....................................................................................... 14 1.3 Methodology ...................................................................................................... 15 1.4 Publication Summary ......................................................................................... 15 1.5 Thesis Outline .................................................................................................... 16 2 Existing tracking, detection and recognition techniques ....................................... 18 2.1 Tracking methods survey ................................................................................... 18 2.1.1 Brief review of the state-of-the-art tracking methods ................................ 18 2.1.2 Technical description of the state-of-the-art methods ................................ 21 2.1.3 Optical flow: the necessary supplement to video object tracking .............. 28 2.2 Detection and recognition methods survey ........................................................ 29 2.2.1 Object detection methods review ............................................................... 32 2.2.2 Neural networks review .............................................................................. 33 2.2.3 Decision trees ............................................................................................. 35 2.2.4 Support Vector Machines ........................................................................... 37 2.2.5 Evolving fuzzy classifiers ........................................................................... 41 2.2.6 Clustering techniques ................................................................................. 44 2.2.7 Image segmentation techniques .................................................................. 49 2.2.8 Template matching techniques ................................................................... 52 5 2.2.9 Feature extraction survey ............................................................................ 54 2.3 Conclusion ......................................................................................................... 59 3 Proposed object tracking techniques ..................................................................... 61 3.1 Practical motivation of the method .................................................................... 61 3.2 Bayesian filter based algorithm for Gaussian mixture propagation ................... 62 3.2.1 System initialisation ................................................................................... 64 3.2.2 Prediction .................................................................................................... 64 3.2.3 Update ......................................................................................................... 66 3.2.4 EM algorithm for the proposed model ....................................................... 67 3.3 Bayesian filter based algorithm with variational inference ............................... 71 3.3.1 Variational inference for the Bayesian filter approximation ...................... 71 3.4 Feature points detection and tracking ................................................................ 77 3.5 Object detection ................................................................................................. 78 3.6 Object co-ordinates estimation combined with Bayesian filter based algorithm 78 3.7 Final formulation of the proposed tracking algorithm ....................................... 80 3.8 Conclusion ......................................................................................................... 81 4 Proposed object detection and recognition techniques .......................................... 83 4.1 Clustering techniques ......................................................................................... 83 4.1.1 TEDA approach overview .......................................................................... 84 4.1.2 Recursive calculation of typicality and eccentricity ................................... 85 4.1.3 Covariance matrix update ........................................................................... 89 4.1.4 TEDACluster .............................................................................................
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