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State-Driven Particle Filter for Multi-person Tracking

  • David Gerónimo Gomez
  • Frédéric Lerasle
  • Antonio M. López Peña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7517)

Abstract

Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential, tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test it in public video sequences.

Keywords

Particle Filter Data Association Track Model Generalize Likelihood Ratio Test Generalize Likelihood Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • David Gerónimo Gomez
    • 1
  • Frédéric Lerasle
    • 2
    • 3
  • Antonio M. López Peña
    • 1
  1. 1.Computer Vision Center and Department of Computer ScienceUniversitat Autònoma de BarcelonaBellaterraSpain
  2. 2.CNRS-LAASToulouseFrance
  3. 3.Université de Toulouse (UPS)ToulouseFrance

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