Jörgen Ahlberg

• Gusland, Daniel; Rolfsjord, Sigmund Johannes Ljosvoll & Ahlberg, Jörgen (2025). Detection-Guided Attention for Selective Target Classification Using Radar Micro-Doppler Spectrograms. IEEE Sensors Journal. ISSN 1530-437X. 25(8), p. 14370–14378. doi: 10.1109/JSEN.2025.3545378. Full text in Research Archive
• Larsen, Martin Vonheim; Rolfsjord, Sigmund Johannes Ljosvoll; Gusland, Daniel; Ahlberg, Jörgen & Mathiassen, Kim (2024). BASE: Probably a Better Approach to Visual Multi-Object Tracking. VISIGRAPP. ISSN 2184-5921. doi: 10.5220/0012386600003660. Full text in Research Archive Show summary

  The field of visual object tracking is dominated by methods that combine simple tracking algorithms and ad hoc schemes. Probabilistic tracking algorithms, which are leading in other fields, are surprisingly absent from the leaderboards. We found that accounting for distance in target kinematics, exploiting detector confidence and modelling non-uniform clutter characteristics is critical for a probabilistic tracker to work in visual tracking. Previous probabilistic methods fail to address most or all these aspects, which we believe is why they fall so far behind current state-of-the-art (SOTA) methods (there are no probabilistic trackers in the MOT17 top 100). To rekindle progress among probabilistic approaches, we propose a set of pragmatic models addressing these challenges, and demonstrate how they can be incorporated into a probabilistic framework. We present BASE (Bayesian Approximation Single-hypothesis Estimator), a simple, performant and easily extendible visual tracker, achie (More)

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