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Non-overlapping Distributed Tracking System Utilizing Particle Filter

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Abstract

Tracking people across multiple cameras is a challenging research area in visual computing, especially when these cameras have non-overlapping field of views. The important task is to associate a current subject with other prior appearances of the same subject across time and space in a camera network. Several known techniques rely on Bayesian approaches to perform the matching task. However, these approaches do not scale well when the dimension of the problem increases; e.g. when the number of subject or possible path increases. The aim of this paper is to propose a unified tracking framework using particle filters to efficiently switch between visual tracking (field of view tracking) and track prediction (non-overlapping region tracking). The particle filter tracking system utilizes a map (known environment) to assist the tracking process when targets leave the field of view of any camera. We implemented and tested this tracking approach in an in-house multiple cameras system as well as using on-line data. Promising results were obtained which suggested the feasibility of such an approach.

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Authors and Affiliations

  1. Department of Computer Science, Curtin University of Technology, GPO Box U1987, Perth, WA, 6001, Australia

    F. L. Lim, W. Leoputra & T. Tan

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  1. F. L. Lim
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  2. W. Leoputra
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  3. T. Tan
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Correspondence to F. L. Lim.

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Lim, F.L., Leoputra, W. & Tan, T. Non-overlapping Distributed Tracking System Utilizing Particle Filter. J VLSI Sign Process Syst Sign Im 49, 343–362 (2007). https://doi.org/10.1007/s11265-007-0091-4

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  • DOI: https://doi.org/10.1007/s11265-007-0091-4

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