Pointcatcher - feature tracking in image sequences

Pointcatcher in use

Pointcatcher is primarily designed to facilitate manual tracking, but also allows provides automation with a correlation-based auto-tracking option. If you are after fully automated feature tracking through large video sequences, Pointcatcher is probably not for you. However, if you are after data extraction from difficult time-lapse imagery, or want interactivity or manual control of tracking, then give it a try. Pointcatcher is written in Matlab and provided as a compiled executable. Pointcatcher files are saved in a Matlab form, but the tracked coordinates can be exported as text to allow easy plotting or spreadsheet analysis if required. Features:

  • video or time-lapse image files
  • track moving features automatically or manually
  • estimation of camera orientation changes with Monte Carlo uncertainties
  • geo-referencing and re-projection of points (and uncertainties) onto DEMs

Pointcatcher dense output


If you use Pointcatcher, please cite this website and some of the previous work listed below.

    pointcatcher v2.0: Please contact me with any bug reports.
    • A major version change that gives Pointcatcher image registration, geo-referencing capabilities and many different types of analysis plots. NOTE: As a result of implementing camera models, the image coordinate system has changed. The image origin is now the top left corner of the top left pixel. In v1.0, this position was (0.5, 0.5), giving the top left pixel a centre of (1, 1).
    • Standalone executable (Matlab not required, but you need the appropriate runtime libraries available from the MathWorks' MATLAB Compiler Runtime (MCR) webpage). Select the R2013b (8.2) version for your particular platform.
    • Windows (64bit): Pointcatcher 2.0 & instructions (.zip)
    • Mac.: Upon request.

      pointcatcher v1.0 (beta):

      Publications using Pointcatcher

      James, M. R. and Robson, S. (2014) Sequential digital elevation models of active lava flows from ground-based stereo time-lapse imagery, ISPRS J. Photogram. Rem. Sens., 97, 160-170, doi:10.1016/j.isprsjprs.2014.08.011

      Applegarth, L. J., Tuffen, H., James, M. R. and Pinkerton, H. (2013) Degassing-induced crystallization in basalts, Earth-Sci. Rev., 116, 1-16, doi:10.1016/j.earscirev.2012.10.007

      Applegarth, L. J., Tuffen, H., James, M. R., Pinkerton, H. and Cashman, K, (2013) Direct observations of degassing induced crystallization in basalts, Geology, 41(2), 243-246, doi:10.1130/G33641.1

      Delcamp, A., van Wyk de Vries, B. & James, M. R. (2011) Relationships between volcano gravitational spreading and magma intrusion. Bull. Volc., doi:10.1007/s00445-011-0558-9

      Applegarth, L. J., James, M. R., van Wyk de Vries, B. & Pinkerton, H. (2010) The influence of surface clinker on the crustal structures and dynamics of 'a'a lava flows. J. Geophys. Res., 115, B07210, doi:10.1029/2009JB006965

      Delcamp, A., van Wyk de Vries, B. & James, M. R. (2008) The influence of edifice slope and substrata on volcano spreading. J. Volc. Geotherm. Res., 117, 925943, doi:10.1016/j.jvolgeores.2008.07.014

      Robson, S. & James, M. R. (2007) Photogrammetric image sequence processing to determine change in active lava flows. Proc. Remote Sensing and Photogrammetry Society Ann. Conf., 2007 (RSPSoc 2007), 11th - 14th September, Newcastle upon Tyne, U.K., 321-322.

      James, M. R., Pinkerton, H & Robson, S. (2007) Image-based measurement of flux variation in distal regions of active lava flows, Geochem. Geophys. Geosys., 8, Q03005, doi:10.1029/2006GC001448

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