PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter

TL;DR — PiVoT is a training-free variational-inference tracker that jointly detects and tracks large, time-varying numbers of objects from noisy radar point clouds, matching deep-learning detectors under heavy clutter without any training. It matters as a scalable, real-time Bayesian alternative to learned detectors in data-scarce settings.

• Extends Poisson-measurement Bayesian tracking with joint inference over object states, shapes, existence probabilities, data association, and measurement rates—no external clustering or detectors needed.
• Efficiency comes from variational innovations: theoretically justified birth pruning, quadratic-to-linear complexity reduction for exact updates, and an efficient Doppler Poisson model handling positional + Doppler measurements.
• Claims real-time operation and scalability to ~1,000 objects on full-scale automotive radar datasets, with robustness to clutter visually inseparable from objects.
• Reports performance comparable to a deep-learning detection benchmark while remaining training-free (per authors; specific metrics not provided in the abstract).