PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter
Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications. Current Bayesian trackers based on Poisson measurement models offer a training-free solution but struggle to achieve accuracy and efficiency under severe clutter, large object populations, and full-resolution Doppler point clouds. We address this with PiVoT, a fast, clutter-resilient multi-object tracker for both positional and Doppler measurements. PiVoT performs end-to-end detection and tracking of a large and time-varying number of objects without external clustering or det
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- FuzzyOverlapping authors or contributors · 62%langchain-ai/langchain →
“Shared author/contributor keys: gan”
- LinkedLinked via arxiv author · 85%Runze Gan →
“PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter”
- LinkedLinked via arxiv author · 85%Yiqing Liang →
“PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter”
- LinkedLinked via arxiv author · 85%Simon J. Godsill →
“PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter”
- LinkedLinked via arxiv author · 85%Mike E. Davies →
“PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter”
- LinkedLinked via arxiv author · 85%James R. Hopgood →
“PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter”