PS-MOT: Cultivating Instance Awareness from Point Seeds for Multi-Object Tracking

We introduce Point-supervised Multi-Object Tracking (PS-MOT) as a cost-effective alternative to traditional bounding box su-pervision, shifting the focus from spatial fitting to topological center-driven representation. However, PS-MOT faces challenges, e.g., spatialambiguity and identity drift due to the lack of explicit geometric struc-ture and scale constraints. To address these, we propose PS-Track, ahierarchical pipeline transitioning from points to instances across data,model, and loss levels. At the data level, we introduce Temporal-FeedbackPrompting (TFP) to evolve points into temporally consistent pseudo-labels using negative spatial cues and motion priors. At the model level,we design the Point-Excited Wavelet Attention (PEWA) module, whichleverages semantic correlations to activate high-frequency components,“hallucinating” object boundaries. At the loss level, Uncertainty-GuidedGaussian Learning (UGL) models pseudo-labels as probabilistic distri-butions, dynamically calibrating supervision intensity. Experiments onDanceTrack, EmboTrack, SportsMOT, and JRDB demonstrate that PS-Track provides a feasible and effective point-supervised alternative acrossdiverse tracking scenarios, establishing a new state-of-the-art for point-supervised tracking. The source code is available at https://github.com/xifen523/PS-MOT.