Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 403-407, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-403-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 403-407, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-403-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Jul 2012

31 Jul 2012

PERSISTENT OBJECT TRACKING WITH RANDOMIZED FORESTS

T. Klinger and D. Muhle T. Klinger and D. Muhle
  • Leibniz Universitaet Hannover, Institute of Photogrammetry and GeoInformation, Nienburger Strasse 1, 30167 Hannover, Germany

Keywords: Learning, Detection, Decision Support, Tracking, Real-time, Video

Abstract. Our work addresses the problem of long-term visual people tracking in complex environments. Tracking a varying number of objects entails the problem of associating detected objects to tracked targets. To overcome the data association problem, we apply a Tracking-by-Detection strategy that uses Randomized Forests as a classifier together with a Kalman filter. Randomized Forests build a strong classifier for multi-class problems through aggregating simple decision trees. Due to their modular setup, Randomized Forests can be built incrementally, which makes them useful for unsupervised learning of object features in real-time. New training samples can be incorporated on the fly, while not drifting away from previously learnt features. To support further analysis of the automatically generated trajectories, we annotate them with quality metrics based on the association confidence. To build the metrics we analyse the confidence values that derive from the Randomized Forests and the similarity of detected and tracked objects. We evaluate the performance of the overall approach with respect to available reference data of people crossing the scene.