A Container-Based Elastic Cloud Architecture for Pseudo Real-Time Exploitation of Wide Area Motion Imagery (WAMI) Stream

Real-time information fusion based on WAMI (Wide-Area Motion Imagery), FMV (Full Motion Video), and text data is highly desired for many mission critical emergency or military applications. However, due to the huge data rate, it is still infeasible to process streaming WAMI in a real-time manner and achieve the goal of online, uninterrupted target tracking. In this paper, a pseudo-real-time Dynamic Data Driven Applications System (DDDAS) WAMI data stream processing scheme is proposed. Taking advantage of the temporal and spatial locality properties, a divide-and-conquer strategy is adopted to overcome the challenge resulting from the large amount of dynamic data. In the Pseudo Real-time Exploitation of Sub-Area (PRESA) framework, each WAMI frame is divided into multiple sub-areas and specified sub-areas are assigned to the virtual machines in a container-based cloud computing architecture, which allows dynamic resource provisioning to meet the performance requirements. A prototype has been implemented and the experimental results validate the effectiveness of our approach.

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  Warren L. Stutzman,et al.  Compact antennas for UWB applications , 2004 .

[3]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[4]  Frederica Darema,et al.  Grid Computing and Beyond: The Context of Dynamic Data Driven Applications Systems , 2005, Proceedings of the IEEE.

[5]  A. G. Amitha Perera,et al.  Evaluation of compression schemes for wide area video , 2008, 2008 37th IEEE Applied Imagery Pattern Recognition Workshop.

[6]  E. Blasch,et al.  Sensor Management Fusion Using Operating Conditions , 2008, 2008 IEEE National Aerospace and Electronics Conference.

[7]  J. D. Morrison,et al.  A framework for activity detection in wide-area motion imagery , 2009, Defense + Commercial Sensing.

[8]  Olga Mendoza-Schrock,et al.  Video image registration evaluation for a layered sensing environment , 2009, Proceedings of the IEEE 2009 National Aerospace & Electronics Conference (NAECON).

[9]  Don R. Hush,et al.  Wide-Area Motion Imagery , 2010, IEEE Signal Processing Magazine.

[10]  Qian Du,et al.  Optical Flow and Principal Component Analysis-Based Motion Detection in Outdoor Videos , 2010, EURASIP J. Adv. Signal Process..

[11]  Qian Du,et al.  A joint optical flow and principal component analysis approach for motion detection , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Guna Seetharaman,et al.  Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video , 2010, 2010 13th International Conference on Information Fusion.

[13]  Mark D. Pritt,et al.  Automated georegistration of motion imagery , 2011, 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[14]  Li Bai,et al.  Visual Tracking Based on Log-Euclidean Riemannian Sparse Representation , 2011, ISVC.

[15]  Li Bai,et al.  Evaluation of visual tracking in extremely low frame rate wide area motion imagery , 2011, 14th International Conference on Information Fusion.

[16]  Erik Blasch,et al.  Joint data management for MOVINT data-to-decision making , 2011, 14th International Conference on Information Fusion.

[17]  Guna Seetharaman,et al.  Wide-area video exploitation (WAVE) joint data management (JDM) for layered sensing , 2011, Defense + Commercial Sensing.

[18]  Kannappan Palaniappan,et al.  Interactive target tracking for persistent wide-area surveillance , 2012 .

[19]  Genshe Chen,et al.  Fast motion detection from airborne videos using graphics processing unit , 2012 .

[20]  John M. Irvine,et al.  Quantifying Interpretability Loss due to Image Compression , 2012 .

[21]  Vijayan K. Asari,et al.  Local Histogram Based Descriptor for Tracking in Wide Area Imagery , 2012 .

[22]  Srinivas Ravela Quantifying Uncertainty for Coherent Structures , 2012, ICCS.

[23]  Erik Blasch,et al.  Context-driven moving vehicle detection in wide area motion imagery , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[24]  Genshe Chen,et al.  Wide-area motion imagery (WAMI) exploitation tools for enhanced situation awareness , 2012, 2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[25]  E. J. Balster,et al.  Detection and tracking performance with compressed wide area motion imagery , 2012, 2012 IEEE National Aerospace and Electronics Conference (NAECON).

[26]  Jongmoo Choi,et al.  Activity recognition in wide aerial video surveillance using entity relationship models , 2012, SIGSPATIAL/GIS.

[27]  Gérard G. Medioni,et al.  Tracking many vehicles in wide area aerial surveillance , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[28]  Li Bai,et al.  Multiple Kernel Learning for vehicle detection in wide area motion imagery , 2012, 2012 15th International Conference on Information Fusion.

[29]  Yi Wu,et al.  Feature-based background registration in wide-area motion imagery , 2012, Defense + Commercial Sensing.

[30]  Zhonghai Wang,et al.  Pattern of life from WAMI objects tracking based on visual context-aware tracking and infusion network models , 2013, Defense, Security, and Sensing.

[31]  Zhonghai Wang,et al.  Spatial context for moving vehicle detection in wide area motion imagery with multiple kernel learning , 2013, Defense, Security, and Sensing.

[32]  Vijayan K. Asari,et al.  Tracking in Wide Area Motion Imagery Using Phase Vector Fields , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[33]  Erik Blasch,et al.  Revisiting the JDL model for information exploitation , 2013, Proceedings of the 16th International Conference on Information Fusion.

[34]  Genshe Chen,et al.  Vehicle detection in wide area aerial surveillance using Temporal Context , 2013, Proceedings of the 16th International Conference on Information Fusion.

[35]  Erik Blasch,et al.  Dynamic Data Driven Applications Systems (DDDAS) modeling for automatic target recognition , 2013, Defense, Security, and Sensing.

[36]  Zhonghai Wang,et al.  Low frame rate video target localization and tracking testbed , 2013, Defense, Security, and Sensing.

[37]  Erik Blasch,et al.  Dynamic Data Driven Applications System Concept for Information Fusion , 2013, ICCS.

[38]  Erik Blasch,et al.  Summary of tracking and identification methods , 2014, Defense + Security Symposium.

[39]  Genshe Chen,et al.  An adaptive process-based cloud infrastructure for space situational awareness applications , 2014, Defense + Security Symposium.

[40]  Genshe Chen,et al.  Summary of methods in Wide-Area Motion Imagery (WAMI) , 2014, Defense + Security Symposium.

[41]  Genshe Chen,et al.  Information fusion in a cloud computing era: A systems-level perspective , 2014, IEEE Aerospace and Electronic Systems Magazine.

[42]  Anthony Hoogs,et al.  Real-time multi-target tracking at 210 megapixels/second in Wide Area Motion Imagery , 2014, IEEE Winter Conference on Applications of Computer Vision.

[43]  Genshe Chen,et al.  A container-based elastic cloud architecture for real-time full-motion video (FMV) target tracking , 2014, 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[44]  Erik Blasch,et al.  Vehicle change detection from aerial imagery using detection response maps , 2014, Defense + Security Symposium.

[45]  Genshe Chen,et al.  A cloud infrastructure for target detection and tracking using audio and video fusion , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).