Tracking multiple objects with multiple sensors is widely recognized to be much more complex than the single-sensor scenario. This contribution proposes a computationally tractable multi-sensor multi-target tracker. Based on Bayes equation and multi-senor observation model, a new corrector for multi-senor is derived. To lower the complexity of update operation, a parallel track-to-measurement association strategy is applied to the corrector. Hypotheses truncation scheme along with first-moment approximation of multi-target density are also employed to improve the tracking efficiency. The tracker is applied to a couple-sensor scenario. Experiment results validate the advantages of proposed method compared to the standard single-sensor δ-generalized labeled multi-Bernoulli filter and the iterated-corrector probability hypothesis density filter.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Centralized multi-sensor multi-target tracking with labeled random finite set


    Contributors:
    Qiu, Hao (author) / Huang, Gaoming (author) / Gao, Jun (author)


    Publication date :

    2017-03-01


    Size :

    8 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English