Improving the fidelity of abstract camera network simulations

  • Arezoo Vejdanparast

    Student thesis: Doctoral ThesisDoctor of Philosophy


    This thesis studies the impact of augmenting an abstract target detection model with a higher degree of realism on the fidelity of the outcomes of camera network simulators in reflecting real-world results. The work is motivated by the identified trade-off between realistic but computationally expensive models and approximate but computationally cheap models. This trade-off opens the possibility for an al-ternative to augment abstract simulation tools with a higher degree of realism to capture both benefits, low computational expense with a higher fidelity of the out-comes.

    For the task of target detection, we propose a novel decomposition method with an intermediate point of representation. This point is the core element of our model that decouples the architecture into two parts. Decoupling brings flexibility and modularity into the design. This empowers practitioners to select the model’s fea-tures individually and independently to their requirements and camera settings. To investigate the fidelity of our model’s outcomes, we build models of three detectors and apply on our lab-based image data set to create ground truth confidences. By incorporating only a few more properties of realism, the fidelity of our model’s out-comes improved significantly when compared to the initial results in reflecting the ground truth confidences.

    Finally, to explore the implication of our high fidelity target detection model, we select a case study from coverage redundancy in smart camera networks. High-lighting the performance of a coverage approach strongly relies on the reliability of target detection results. An underestimation in the performance of studied coverage approaches is determined by employing the standard abstract detection model when compared to the results of our model.

    The identified underestimation in this study is one example of the general open concern in agent-based modelling about the unclear impact of simplified abstract models on the ability of the simulator to capture real-world behaviours.
    Date of AwardSept 2020
    Original languageEnglish
    SupervisorPeter Lewis (Supervisor)


    • Simulators
    • Target Detection
    • Agent-based Modelling

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