Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

Filters

2
1
1 1
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction
    ( 2021)
    Hug, Ronny
    ;
    Becker, Stefan
    ;
    Hübner, Wolfgang
    ;
    Arens, Michael
    Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation is proposed. The dataset representation is obtained by first employing a non-trivial spatial sequence alignment, which enables a subsequent learning vector quantization (LVQ) stage. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets, followed by a brief discussion on indications for human trajectory prediction and benchmarking.
  • Publication
    State estimation for tracking in image space with a de- and re-coupled IMM filter
    ( 2018)
    Becker, Stefan
    ;
    Hübner, Wolfgang
    ;
    Arens, Michael
    Estimating the motion state of objects is a central component of most visual tracking pipelines. Visual tracking relies on observations in scale space generated by an appearance model. Under real-life conditions, it is obvious to assume that the dynamic of a tracked object changes over time. A popular solution for considering such varying system characteristics is the Interacting Multiple Model (IMM) filter. Usually, the motion of objects is modeled using position, velocity, and acceleration. Although it seems obvious that different image space dimensions can be combined in one overall system state, this naïve approach may fail under various circumstances. Toward this end, we demonstrate the benefit of decoupling the state estimate of an IMM filter in case of relying solely on the output of a visual tracker. Further, a state re-coupling scheme is introduced which helps to better deal with the corresponding measurement uncertainties of such a tracking pipeline. The proposed decoupled and re-coupled IMM filters are evaluated on publicly available datasets.