Tracking of motor vehicles from aerial video imagery using the OT-MACH correlation filter

Accurately tracking moving targets in a complex scene involving moving cameras, occlusions and targets embedded in noise is a very active research area in computer vision. In this paper, an optimal trade-off maximum correlation height (OT-MACH) filter has been designed and implemented as a robust tracker. The algorithm allows selection of different objects as a target, based on the operator’s requirements. The user interface is designed so as to allow the selection of a different target for tracking at any time. The filter is updated, at a frequency selected by the user, which makes the filter more resistant to progressive changes in the object’s orientation and scale. The tracker has been tested on both colour visible band as well as infra-red band video sequences acquired from the air by the Sussex County police helicopter. Initial testing has demonstrated the ability of the filter to maintain a stable track on vehicles despite changes of scale, orientation and lighting and the ability to re-acquire the track after short losses due to the vehicle passing behind occlusions.

[1]  D. Casasent,et al.  Minimum average correlation energy filters. , 1987, Applied optics.

[2]  C R Chatwin,et al.  Application of nonlinearity to wavelet-transformed images to improve correlation filter performance. , 1997, Applied optics.

[3]  C R Chatwin,et al.  Synthetic discriminant function filter employing nonlinear space-domain preprocessing on bandpass-filtered images. , 1998, Applied optics.

[4]  Chris R. Chatwin,et al.  Sensor Geometry and Sampling Methods for Space-Variant Image Processing , 2002, Pattern Analysis and Applications.

[5]  Chris Chatwin,et al.  Synthetic discriminant function fringe-adjusted joint transform correlator , 1995 .

[6]  Chris R. Chatwin,et al.  A Fast Hough Transform for the Parametrisation of Straight Lines using Fourier Methods , 2000, Real Time Imaging.

[7]  C Chatwin,et al.  Two-pixel computer-generated hologram with a zero-twist nematic liquid-crystal spatial light modulator. , 2000, Optics letters.

[8]  B. Kumar,et al.  Generalized synthetic discriminant functions , 1988 .

[9]  B. V. Vijaya Kumar,et al.  Unconstrained correlation filters. , 1994, Applied optics.

[10]  B. V. Vijaya Kumar,et al.  Minimum-variance synthetic discriminant functions , 1986 .

[11]  Chris Chatwin,et al.  Position, rotation, scale, and orientation invariant object tracking from cluttered scenes , 2006, SPIE Defense + Commercial Sensing.

[12]  Chris Chatwin,et al.  Fully invariant object recognition in cluttered scenes , 2005, Other Conferences.

[13]  Philip Birch,et al.  Optical and electronic design of a hybrid digital-optical correlator system , 2002 .

[14]  C. Chatwin,et al.  Inactivation of bacteria and yeasts on agar surfaces with high power Nd: YAG laser light , 1996, Letters in applied microbiology.

[15]  Ruikang K. Wang,et al.  Assessment of a Wiener filter synthetic discriminant function for optical correlation , 1995 .

[16]  Tien-Hsin Chao,et al.  MACH filter synthesizing for detecting targets in cluttered environment for grayscale optical correlator , 1999, Defense, Security, and Sensing.

[17]  Chris R. Chatwin,et al.  A framework for post-event timeline reconstruction using neural networks , 2007, Digit. Investig..

[18]  Mark S. Nixon,et al.  Feature Extraction and Image Processing , 2002 .

[19]  Chris Chatwin,et al.  Position-, rotation-, scale-, and orientation-invariant multiple object recognition from cluttered scenes , 2006 .

[20]  Philip Birch,et al.  Tracking Moving Objects Using Bandpass Filter Enhanced Localisation and Automated Initialisation of Active Contour Snakes , 2010 .

[21]  Philip Birch,et al.  An adaptive sample count particle filter , 2012, Comput. Vis. Image Underst..

[22]  P Birch,et al.  Dynamic complex wave-front modulation with an analog spatial light modulator. , 2001, Optics letters.