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ARXIV:2603.15812 · MULTI-OBJECT TRACKING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15812MULTI-OBJECT TRACKINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
ModTrack is a modular, sensor-agnostic multi-view tracking system that enhances identity consistency and generalization across various sensor modalities.
Opportunity summary
Pain ModTrack is a modular, sensor-agnostic multi-view tracking system that enhances identity consistency and generalization across various sensor modalities.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
ModTrack is a modular, sensor-agnostic multi-view tracking system that enhances identity consistency and generalization across various sensor modalities. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time.
Multi-View Multi-Object Tracking (MV-MOT) aims to localize and maintain consistent identities of objects observed by multiple sensors. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. ModTrack achieves 95.5 IDF1 and 91.4 MOTA on \textit{WildTrack}, surpassing all prior modular methods by over 21 points and rivaling the state-of-the-art end-to-end methods…
Multi-Object Tracking moved forward this cycle; last verified April 2026. Public score 7.0/10.
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ModTrack is a modular, sensor-agnostic multi-view tracking system that enhances identity consistency and generalization across various sensor modalities.
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10.48550/arXiv.2603.15812ModTrack is a modular, sensor-agnostic multi-view tracking system that enhances identity consistency and generalization across various sensor modalities.
Abstract
Multi-View Multi-Object Tracking (MV-MOT) aims to localize and maintain consistent identities of objects observed by multiple sensors. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time. Recent end-to-end approaches address this by jointly learning 2D Bird's Eye View (BEV) representations and identity associations, achieving high tracking accuracy. However, these methods offer no principled uncertainty accounting and remain tightly coupled to their training configuration, limiting generalization across sensor layouts, modalities, or datasets without retraining. We propose ModTrack, a modular MV-MOT system that matches end-to-end performance while providing cross-modal, sensor-agnostic generalization and traceable uncertainty. ModTrack confines learning methods to just the \textit{Detection and Feature Extraction} stage of the MV-MOT pipeline, performing all fusion, association, and tracking with closed-form analytical methods. Our design reduces each sensor's output to calibrated position-covariance pairs $(\mathbf{z}, R)$; cross-view clustering and precision-weighted fusion then yield unified estimates $(\hat{\mathbf{z}}, \hat{R})$ for identity assignment and temporal tracking. A feedback-coupled, identity-informed Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with HMM motion modes uses these fused estimates to maintain identities under missed detections and heavy occlusion. ModTrack achieves 95.5 IDF1 and 91.4 MOTA on \textit{WildTrack}, surpassing all prior modular methods by over 21 points and rivaling the state-of-the-art end-to-end methods while providing deployment flexibility they cannot. Specifically, the same tracker core transfers unchanged to \textit{MultiviewX} and \textit{RadarScenes}, with only perception-module replacement required to extend to new domains and sensor modalities.
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Dimensions overall score 7.0
PROBLEM
ModTrack is a modular, sensor-agnostic multi-view tracking system that enhances identity consistency and generalization across various sensor modalities. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time.
METHOD
Multi-View Multi-Object Tracking (MV-MOT) aims to localize and maintain consistent identities of objects observed by multiple sensors. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. ModTrack achieves 95.5 IDF1 and 91.4 MOTA on \textit{WildTrack}, surpassing all prior modular methods by over 21 points and rivaling the state-of-the-art end-to-end methods while providing deployment flex...
WHY NOW
Multi-Object Tracking moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
ModTrack is a modular, sensor-agnostic multi-view tracking system that enhances identity consistency and generalization across various sensor modalities. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-View Multi-Object Tracking (MV-MOT) aims to localize and maintain consistent identities of objects observed by multiple sensors. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. ModTrack achieves 95.5 IDF1 and 91.4 MOTA on \textit{WildTrack}, surpassing all prior modular methods by over 21 points and rivaling the state-of-the-art end-to-end methods while providing deployment flexibility they cannot.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-Object Tracking moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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ModTrack is a modular, sensor-agnostic multi-view tracking system that enhances identity consistency and generalization across various sensor modalities.
Segment
Multi-Object Tracking
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