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Application of Cortical Learning Algorithms to Movement Classification Towards Automated Video Forensics

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Application of Cortical Learning Algorithms to Movement Classification Towards Automated Video Forensics Application of Cortical Learning Algorithms to Movement Classification towards Automated Video Forensics By Abdullah Alshaikh A Thesis submitted in partial fulfilment of the requirement for the degree of Doctor of Philosophy at Staffordshire University, Stafford, United Kingdom February 2019 Declaration I, Abdullah Alshaikh, declare that all work submitted is my own, that any other external material used within my report my report is documented and referenced fully. Date: ............................................. Signature:....................................... ii Abstract The need for proper and acceptable video forensics process is necessary due to the proliferation and advancement of video technologies in all aspects of our life, hence the legal system has heavily invested in this area. Also, the classification of the movements of objects detected from a video feed is an essential module to automate the forensic video process. Recently, new bio- inspired machine learning techniques have been proposed in the attempt of mimicking the function of the human brain. Hierarchical Temporal Memory (HTM) theory has proposed new computational learning models, Cortical Learning Algorithms (CLA), inspired from the neocortex, which offer a better understanding of how our brains function. This research aims to study the requirements of video forensic investigation and Police procedures to propose a new semi-automated post-incident analysis framework and to investigate the application of the CLA to movement classification towards an automated video forensics process. This research starts by reviewing the research related to police practices for video forensics as well as various proposed video forensic frameworks. Then a Questionnaire targeting CCTV/Video Forensics practitioners has been developed to capture the requirements for automating the video forensics process, this has been followed by proposing a new post-incident analysis framework. Then, a literature review covering state-of-the-art movement classification algorithms has been carried out. Finally, a novel CLA-based movement classification algorithm has been proposed and devised to classify the movements of moving objects in realistic video surveillance scenarios, and the test results have been evaluated. Tests applied on twenty-three videos have been conducted to detect movement anomalies in different scenarios. Additionally, in this study, the proposed algorithm has been evaluated and compared against several state-of-the-art anomaly detection algorithms. The proposed algorithm has achieved 66.29% average F-measure, with an improvement of 15.5%compared to the k- Nearest Neighbour Global Anomaly Score (kNN-GAS) algorithm. The Independent Component Analysis-Local Outlier Probability (ICA-LoOP) scored 42.75%, the Singular Value Decomposition Influence Outlier (SVD-IO) achieved 34.82%, whilst the Connectivity Based Factor algorithm (CBOF) scored 8.72%. The proposed models, which are based on HTM, have empirically portrayed positive potential and had exceeded in performance when compared to state-of-the-art algorithms. iii List of Publications This research activity has led to several publications in international journals. These are summarized below: 1. AlShaikh, A. and Sedky, M., (2015). Post Incident Analysis Framework for Automated Video Forensic Investigation. International Journal of Computer Applications, 129(17), pp. 38-44. 2. Alshaikh, A., & Sedky, M. (2016). Movement Classification Technique for Video Forensic Investigation. International Journal of Computer Applications, 135(12), pp. 1-7. ‏ 3. AlShaikh, A. and Sedky, M., (2017). A Cortical Learning Movement Classification Algorithm for Video Surveillance. International Journal of Computer Applications 6(12), pp. 501-510. 4. AlShaikh, A. and Sedky, M., (2019), Application of Cortical Learning Algorithms to Movement Classification, International Journal of Computer Applications 8(3), pp. 58- 65. iv Acknowledgements First of all, I would like to thank God Almighty for reconciling me in my whole life. Then I would like to express my thanks to the honourable man who is made of great knowledge and ethics, my supervisor Dr Mohamed Sedky. He has made great contributions to this research project since it was only an idea until it developed and became what it is now. I would also like to thank the great University of Staffordshire and the great professors for their great role in supporting research and researchers for the advancement of science not only in the UK but throughout the world, also to my wonderful mother and father who blessed me every step of my life. My precious wife and beloved children who accompanied me throughout the course of my life. I would also like to thank all the officials responsible for the university library that provided me the material necessary for this research thesis. I thank all those who stood beside me. v List of Contents Declaration ................................................................................................................................................... ii Abstract ....................................................................................................................................................... iii List of Publications ..................................................................................................................................... iv Acknowledgements ...................................................................................................................................... v List of Contents .......................................................................................................................................... vi List of Figures .............................................................................................................................................. x List of Tables ............................................................................................................................................. xii Abbreviations............................................................................................................................................ xiii Chapter 1 ..................................................................................................................................................... 1 Introduction ................................................................................................................................................. 1 1.1 Context ............................................................................................................................................ 1 1.2 Project Aim ..................................................................................................................................... 3 1.3 Objectives........................................................................................................................................ 3 1.4 Scope of the Investigation ............................................................................................................... 4 1.5 Research Approach ......................................................................................................................... 6 1.5.1 Evaluation of the proposed post-incident analysis framework ................................................... 9 1.5.2 Evaluation of the proposed movement classification technique ................................................. 9 1.6 Research questions........................................................................................................................ 10 1.7 Contribution to knowledge ............................................................................................................ 10 1.8 Thesis structure ............................................................................................................................. 11 Chapter 2 ................................................................................................................................................... 14 Video Forensic ........................................................................................................................................... 14 2.1 Introduction .................................................................................................................................. 14 2.2 Background ................................................................................................................................... 14 2.3 Digital Forensic ............................................................................................................................ 16 2.3.1 Digital Evidence ....................................................................................................................... 20 2.3.2 Characteristics of Digital Evidence .......................................................................................... 20 2.3.3 Role of Digital Evidence ........................................................................................................... 21 2.3.4 Legal Requirements .................................................................................................................. 22 2.3.5 Legal Evidence ......................................................................................................................... 24 2.4 The Forensic Models ....................................................................................................................
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