Detection of Aircrafts on a Collision Course using Spatio-Temporal HOG

We have developed a method for the detection of both generic flight neighbouring aircrafts and those on a collision course. Our approach employs a sliding window linear Support Vector Machine (SVM) classifier with a Histogram of Oriented Gradients (HOG) feature representation. An extension of this approach to the spatio-temporal domain is also considered and we demonstrate its advantage for the detection of aircrafts on a collision path. We evaluated our approach for the detection of both small rotorcraft and larger fixed-wing aircrafts in challenging video sequences. Our results show that aircrafts on a collision course can be detected more reliably than when assuming a generic flight path. This is very interesting in practice, since this case is of critical importance. We also show that our spatio-temporal approach improves the detection accuracy with respect to conventional single- frame approaches.

[1]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  Mei-Chen Yeh,et al.  Fast Human Detection Using a Cascade of Histograms of Oriented Gradients , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Alexei A. Efros,et al.  Ensemble of exemplar-SVMs for object detection and beyond , 2011, 2011 International Conference on Computer Vision.

[4]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[5]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[6]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[8]  Cordelia Schmid,et al.  A Spatio-Temporal Descriptor Based on 3D-Gradients , 2008, BMVC.

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

[10]  Pascal Fua,et al.  Making Action Recognition Robust to Occlusions and Viewpoint Changes , 2010, ECCV.