TrackFlow: Multi-Object Tracking with Normalizing Flows
Gianluca Mancusi Aniello Panariello Angelo Porrello Matteo Fabbri Simone Calderara Rita Cucchiara
The field of multi-object tracking has recently seen a renewed interest in
the good old schema of tracking-by-detection, as its simplicity and strong
priors spare it from the complex design and painful babysitting of
tracking-by-attention approaches. In view of this, we aim at extending
tracking-by-detection to multi-modal settings, where a comprehensive cost has
to be computed from heterogeneous information e.g., 2D motion cues, visual
appearance, and pose estimates. More precisely, we follow a case study where a
rough estimate of 3D information is also available and must be merged with
other traditional metrics (e.g., the IoU). To achieve that, recent approaches
resort to either simple rules or complex heuristics to balance the contribution
of each cost. However, i) they require careful tuning of tailored
hyperparameters on a hold-out set, and ii) they imply these costs to be
independent, which does not hold in reality. We address these issues by
building upon an elegant probabilistic formulation, which considers the cost of
a candidate association as the negative log-likelihood yielded by a deep
density estimator, trained to model the conditional joint probability
distribution of correct associations. Our experiments, conducted on both
simulated and real benchmarks, show that our approach consistently enhances the
performance of several tracking-by-detection algorithms.