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ARXIV:2602.14771 · OBJECT TRACKING · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE
ARXIV:2602.14771OBJECT TRACKINGSUBMITTED 19 MAR · 21:31 UTCFRESHNESS STALEarXiv
An AI-powered object tracking framework enhancing visibility estimation for improved dynamic environment adaptation and occlusion handling.
Opportunity summary
Pain An AI-powered object tracking framework enhancing visibility estimation for improved dynamic environment adaptation and occlusion handling.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence failed
An AI-powered object tracking framework enhancing visibility estimation for improved dynamic environment adaptation and occlusion handling. In contrast, recent generic object trackers are often optimized for training targets, which limits robustness and generalization in…
The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity. In contrast, recent generic object trackers are…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Conditioned on object priors iteratively generated by the tracker, OccuSolver incrementally refines visibility states, strengthens occlusion handling, and produces higher-quality reference labels that progressively…
Object Tracking moved forward this cycle; last verified April 2026. Public score 8.0/10.
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An AI-powered object tracking framework enhancing visibility estimation for improved dynamic environment adaptation and occlusion handling.
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10.48550/arXiv.2602.14771An AI-powered object tracking framework enhancing visibility estimation for improved dynamic environment adaptation and occlusion handling.
Abstract
The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity. In contrast, recent generic object trackers are often optimized for training targets, which limits robustness and generalization in unseen scenarios, and their occlusion reasoning remains coarse, lacking detailed modeling of occlusion patterns. To address these limitations in generalization and occlusion perception, we propose GOT-JEPA, a model-predictive pretraining framework that extends JEPA from predicting image features to predicting tracking models. Given identical historical information, a teacher predictor generates pseudo-tracking models from a clean current frame, and a student predictor learns to predict the same pseudo-tracking models from a corrupted version of the current frame. This design provides stable pseudo supervision and explicitly trains the predictor to produce reliable tracking models under occlusions, distractors, and other adverse observations, improving generalization to dynamic environments. Building on GOT-JEPA, we further propose OccuSolver to enhance occlusion perception for object tracking. OccuSolver adapts a point-centric point tracker for object-aware visibility estimation and detailed occlusion-pattern capture. Conditioned on object priors iteratively generated by the tracker, OccuSolver incrementally refines visibility states, strengthens occlusion handling, and produces higher-quality reference labels that progressively improve subsequent model predictions. Extensive evaluations on seven benchmarks show that our method effectively enhances tracker generalization and robustness.
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PROBLEM
An AI-powered object tracking framework enhancing visibility estimation for improved dynamic environment adaptation and occlusion handling. In contrast, recent generic object trackers are often optimized for training targets, which limits robustness and generalization in unseen...
METHOD
The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity. In contrast, recent generic object trackers are often optimized for training...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Conditioned on object priors iteratively generated by the tracker, OccuSolver incrementally refines visibility states, strengthens occlusion handling, and produces higher-quality reference labels that pro...
WHY NOW
Object Tracking moved forward this cycle; last verified April 2026. Public score 8.0/10.
we propose GOT-JEPA, a model-predictive pretraining framework that extends JEPA from predicting image features to predicting tracking models.
Directly stated in the abstract and analysis with clear methodological description.
partial
This design provides stable pseudo supervision and explicitly trains the predictor to produce reliable tracking models under occlusions, distractors, and other adverse observations, improving generalization to dynamic environments.
Explicitly stated in the abstract as a core contribution, supported by evaluation across multiple benchmarks.
partial
OccuSolver adapts a point-centric point tracker for object-aware visibility estimation and detailed occlusion-pattern capture.
Directly stated in the abstract and analysis with specific technical details.
partial
Extensive evaluations on seven benchmarks show that our method effectively enhances tracker generalization and robustness.
Directly stated in the abstract and supported by method_eval analysis.
partial
The technique may still face challenges in extremely dense occlusion scenarios where object features are entirely hidden.
Explicitly stated in the analysis caveats section as a limitation.
partial
Additionally, its dependency on pre-existing frameworks poses integration limitations in bespoke systems.
Directly stated in the analysis caveats section as a limitation.
partial
This framework can replace conventional object trackers that struggle with dynamic and occluded environments, potentially transforming fields like autonomous navigation, smart retail, and video surveillance.
Stated in the disruption analysis but represents potential impact rather than demonstrated result.
partial
OccuSolver incrementally refines visibility states, strengthens occlusion handling, and produces higher-quality reference labels that progressively improve subsequent model predictions.
Directly stated in the abstract with specific technical description.
partial
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An AI-powered object tracking framework enhancing visibility estimation for improved dynamic environment adaptation and occlusion handling.
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Object Tracking
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