Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

Filters

3
3
2 1
3
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    Utilizing Dataset Affinity Prediction in Object Detection to Assess Training Data
    ( 2024)
    Becker, Stefan
    ;
    Bayer, Jens
    ;
    Hug, Ronny
    ;
    Hübner, Wolfgang
    ;
    Arens, Michael
    Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is not straightforward and may even be counterproductive. Assessing the effectiveness of pooling datasets in a principled manner is challenging due to the difficulty in estimating the overall information content of individual datasets. Towards this end, we propose incorporating a data source prediction module into standard object detection pipelines. The module runs with minimal overhead during inference time, providing additional information about the data source assigned to individual detections. We show the benefits of the so-called dataset affinity score by automatically selecting samples from a heterogeneous pool of vehicle datasets. The results show that object detectors can be trained on a significantly sparser set of training samples without losing detection accuracy. T
  • Publication
    Center point-based feature representation for tracking
    ( 2023)
    Becker, Stefan
    ;
    Hug, Ronny
    ;
    Hübner, Wolfgang
    ;
    Arens, Michael
    Center points are commonly the results of anchor-free object detectors. Starting from this initial representation, a regression scheme is utilized to determine a target point set to capture object properties such as enclosing bounding boxes and further attributes such as class labels. When only trained for the detection tasks, the encoded center point feature representations are not well suited for tracking objects since the embedded features are not stable over time. To tackle this problem, we present an approach of joint detection and feature embedding for multiple object tracking. The proposed approach applies an anchor-free detection model to pairs of images to extract single-point feature representations. To generate temporal stable features which are suitable for track association across short time intervals, auxiliary losses are applied to reduce the distance of tracked identities in the embedded feature space. The abilities of the presented approach are demonstrated on real-world data reflecting prototypical object tracking scenarios.
  • Publication
    MissFormer: (In-)Attention-Based Handling of Missing Observations for Trajectory Filtering and Prediction
    ( 2021)
    Becker, Stefan
    ;
    Hug, Ronny
    ;
    Hübner, Wolfgang
    ;
    Arens, Michael
    ;
    Morris, Brendan Tran
    In applications such as object tracking, time-series data inevitably carry missing observations. Following the success of deep learning-based models for various sequence learning tasks, these models increasingly replace classic approaches in object tracking applications for inferring the objects' motion states. While traditional tracking approaches can deal with missing observations, most of their deep counterparts are, by default, not suited for this. Towards this end, this paper introduces a transformer-based approach for handling missing observations in variable input length trajectory data. The model is formed indirectly by successively increasing the complexity of the demanded inference tasks. Starting from reproducing noise-free trajectories, the model then learns to infer trajectories from noisy inputs. By providing missing tokens, binary-encoded missing events, the model learns to in-attend to missing data and infers a complete trajectory conditioned on the remaining inputs. In the case of a sequence of successive missing events, the model then acts as a pure prediction model. The abilities of the approach are demonstrated on synthetic data and real-world data reflecting prototypical object tracking scenarios.