Application of an image feature network-based object recognition algorithm to aircraft detection and classification

A network created from the distance-values representing the spacing between points identified by an image feature detection algorithm can be utilized for object classification. This paper presents work on the application of this algorithm to the problem of aircraft presence detection and classification. It considers algorithm performance across a variety of scenarios, including instances where the sky has different characteristics, detection and characterization from different levels of image resolution and detection and characterization where multiple craft are present in a single frame. An extension to the base algorithm, which determines the orientation of a detected aircraft is also presented.

[1]  Christophe Cruz,et al.  From 3D Point Clouds to Semantic Objects - An Ontology-based Detection Approach , 2011, KEOD.

[2]  Luo Juan,et al.  A comparison of SIFT, PCA-SIFT and SURF , 2009 .

[3]  R. A. Mitchell,et al.  Robust statistical feature based aircraft identification , 1999 .

[4]  Demetri Psaltis,et al.  Correlation filters for aircraft identification from radar range profiles , 1993 .

[5]  Nicholas Roy,et al.  Indoor scene recognition by a mobile robot through adaptive object detection , 2013, Robotics Auton. Syst..

[6]  Andrew Zisserman,et al.  Image Classification using Random Forests and Ferns , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[8]  Harri Ehtamo,et al.  Visual Aircraft Identification as a Pursuit-Evasion Game , 2000 .

[9]  I. Dror,et al.  Object identification as a function of discriminability and learning presentations: the effect of stimulus similarity and canonical frame alignment on aircraft identification. , 2000, Journal of experimental psychology. Applied.

[10]  S. P. Shinde,et al.  IMPLEMENTATION OF PATTERN RECOGNITION TECHNIQUES AND OVERVIEW OF ITS APPLICATIONS IN VARIOUS AREAS OF ARTIFICIAL INTELLIGENCE , 2011 .

[11]  Jeremy Straub.  Detection of small targets and their characterization based on their formation using an image feature network-based object recognition algorithm , 2014, Defense + Security Symposium.

[12]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[13]  Robert B. McGhee,et al.  Aircraft Identification by Moment Invariants , 1977, IEEE Transactions on Computers.

[14]  Jesús García,et al.  Aircraft identification integrated into an airport surface surveillance video system , 2004, Machine Vision and Applications.

[15]  Stephen Gould,et al.  Discriminative Learning with Latent Variables for Cluttered Indoor Scene Understanding , 2010, ECCV.

[16]  Sai Ravela,et al.  Deformation invariant image matching by spectrally controlled diffeomorphic alignment , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Tomaso Poggio,et al.  Models of object recognition , 2000, Nature Neuroscience.