Wide-Area Imagery

ide-area motion imagery (WAMI) sensors are placed on helicopters, balloons, small aircraft, or unmanned aerial vehicles and are used to image small citysized areas at approximately 0.5 m/pixel and about one or two frames/s. The geospatial-temporal data sets produced by these systems allow for the observation of many dynamic phenomena that were previously inaccessible in street-level video data, but the efficient exploitation of this data poses significant technical challenges for image and video analysis and for data mining. Content of interest is defined in very abstract terms related to how humans interpret video imagery, but the data is defined in very physical terms related to the imaging device. This difference in representations is often called the semantic gap. In this review article, we describe advances that have been made and the advances that will be needed to produce the hierarchy of computational models required to narrow the semantic gap in WAMI.

[1]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[2]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[3]  Wei-Ying Ma,et al.  Learning a semantic space from user's relevance feedback for image retrieval , 2003, IEEE Trans. Circuits Syst. Video Technol..

[4]  Jitendra Malik,et al.  Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

[6]  Terry Caelli,et al.  Shape Tracking and Production Using Hidden Markov Models , 2001, Int. J. Pattern Recognit. Artif. Intell..

[7]  Ralf Hartmut Güting,et al.  Moving Objects Databases , 2005 .

[8]  Amir Averbuch,et al.  Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .

[9]  Neal R. Harvey,et al.  A change detection approach to moving object detection in low fame-rate video , 2009, Defense + Commercial Sensing.

[10]  Edward Rosten,et al.  Improving multiple target tracking in structured environments using velocity priors , 2008, SPIE Defense + Commercial Sensing.

[11]  Uwe Soergel,et al.  AIRBORNE MONITORING OF VEHICLE ACTIVITY IN URBAN AREAS , 2004 .

[12]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[13]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[14]  A. Hampapur,et al.  Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking , 2005, IEEE Signal Processing Magazine.

[15]  Anthony Hoogs,et al.  Event Recognition with Fragmented Object Tracks , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[16]  Edward Y. Chang,et al.  Active Learning for Interactive Multimedia Retrieval , 2008, Proceedings of the IEEE.

[17]  Dan Schonfeld,et al.  Real-Time Distributed Multi-Object Tracking Using Multiple Interactive Trackers and a Magnetic-Inertia Potential Model , 2007, IEEE Transactions on Multimedia.

[18]  Jonathan D. Cohen,et al.  GPU-accelerated hierarchical dense correspondence for real-time aerial video processing , 2009, 2009 Workshop on Motion and Video Computing (WMVC).

[19]  Matthew Brand,et al.  Discovery and Segmentation of Activities in Video , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Svetha Venkatesh,et al.  Tracking and Surveillance in Wide-Area Spatial Environments Using the Abstract Hidden Markov Model , 2001, Int. J. Pattern Recognit. Artif. Intell..

[21]  Carol Traynor,et al.  Putting power in the hands of end users: a study of programming by demonstration, with an application to geographical information systems , 1998, CHI Conference Summary.

[22]  William Wright,et al.  GeoTime Information Visualization , 2005, Inf. Vis..

[23]  King Ngi Ngan,et al.  A memory learning framework for effective image retrieval , 2005, IEEE Transactions on Image Processing.

[24]  A.G.A. Perera,et al.  Learning Motion Patterns in Surveillance Video using HMM Clustering , 2008, 2008 IEEE Workshop on Motion and video Computing.

[25]  Shaogang Gong,et al.  Recognition of group activities using dynamic probabilistic networks , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[26]  Stefan Wrobel,et al.  Visual analytics tools for analysis of movement data , 2007, SKDD.

[27]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[28]  Jason Dykes,et al.  Seeking structure in records of spatio-temporal behaviour: visualization issues, efforts and applications , 2003, Comput. Stat. Data Anal..

[29]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[30]  Claude Sammut,et al.  A Framework for Behavioural Cloning , 1995, Machine Intelligence 15.

[31]  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).

[32]  Andrew W. Moore,et al.  Active Learning for Anomaly and Rare-Category Detection , 2004, NIPS.

[33]  Hyrum S. Anderson,et al.  Aerial video and ladar imagery fusion for persistent urban vehicle tracking , 2007, SPIE Defense + Commercial Sensing.

[34]  Kristin A. Cook,et al.  Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .

[35]  Joseph L. Mundy,et al.  Augmenting Shape with Appearance in Vehicle Category Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[36]  James Theiler,et al.  Quantitative comparison of quadratic covariance-based anomalous change detectors. , 2008, Applied optics.