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A Particle Filtering Approach for Tracking an Unknown Number of Objects with Dynamic Relations

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Journal of Mathematical Modelling and Algorithms in Operations Research

Abstract

In recent years there has been a growing interest on particle filters for solving tracking problems, thanks to their applicability to problems with continuous, non-linear and non-Gaussian state spaces, which makes them more suited than hidden Markov models, Kalman filters and their derivations, in many real world tasks. Applications include video surveillance, sensor fusion, tracking positions and behaviors of moving objects, situation assessment in civil and bellic scenarios, econometric and clinical data series analysis. In many environments it is possible to recognize classes of similar entities, like pedestrians or vehicles in a video surveillance system, or commodities in econometric. In this paper, a relational particle filter for tracking an unknown number of objects is presented which exploits possible interactions between objects to improve the quality of filtering. We will see that taking into account relations between objects will ease the tracking of objects in presence of occlusions and discontinuities in object dynamics. Experimental results on a benchmark data set are presented.

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Authors and Affiliations

  1. MIND Laboratory, DISCo Computer Science Department, University of Milano-Bicocca, Viale Sarca 336, 20100, Milano, Italy

    Luca Cattelani & Enza Messina

  2. Image Group, E-Science Centre, Department of Computer Science (DIKU), University of Copenhagen, Universitetsparken 5, 2100, Copenhagen, Denmark

    Cristina Manfredotti

Authors
  1. Luca Cattelani
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  2. Cristina Manfredotti
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  3. Enza Messina
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Correspondence to Luca Cattelani.

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Cattelani, L., Manfredotti, C. & Messina, E. A Particle Filtering Approach for Tracking an Unknown Number of Objects with Dynamic Relations. J Math Model Algor 13, 3–21 (2014). https://doi.org/10.1007/s10852-012-9213-5

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  • DOI: https://doi.org/10.1007/s10852-012-9213-5

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