Gaussian Process Assisted Labeling for Video Object Tracking: An Analysis - Maia, Felipe J. A.; Maia, José E. B. (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2026)
The development of datasets for video object tracking is inherently resource-intensive, primarily due to the requirement of exhaustive frame-by-frame annotation of target positions. Various strategies have been explored to mitigate these costs. This study evaluates the effectiveness of Gaussian Process (GP) Regression as an auxiliary tool for annotating target trajectories within video tracking benchmarks. By leveraging manual ground-truth data sampled at intervals of k frames, GP models are employed to infer the coordinates for the intervening unlabeled frames. Experimental evaluations conducted on Multi-Object Tracking (MOT) datasets demonstrate a mean Area Under the Curve (AUC) exceeding 90% at k = 20 for sequences characterized by uniform target dynamics and stationary camera platforms. Furthermore, the proposed approach sustained a mean precision higher than 80% at same k even when subjected to more challenging and unconstrained scenarios.