SAMIDARE: Advanced Tracking-by-Segmentation for Dense Scenarios

2026-04-23

AI Summary

In dense scenes, segmentation-based multi-object tracking suffers from degraded robustness due to mask errors and frequent identity switches. To address these challenges, this work proposes the SAM2MOT framework, which enhances feature completeness under occlusion through density-aware mask regeneration, mitigates frequent target disappearance via selective memory updating, and improves trajectory consistency with a state-aware association mechanism. Evaluated on the SportsMOT validation set, the proposed method achieves state-of-the-art performance, yielding a 2.5-point improvement in HOTA and a 4.2-point gain in IDF1 over previous approaches.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Automated sports analysis demands robust multi-object tracking (MOT), yet segmentation-based methods often struggle with mask errors and ID switches in dense scenes. We propose SAMIDARE, a framework that enhances SAM2MOT for crowded scenes through three key components: (1) density-aware mask re-generation and (2) selective memory updates, both for adaptive mask control to preserve target feature integrity, and (3) state-aware association and new track initialization, which improves robustness under mutual occlusions and frequent frame-out events. Evaluated on the SportsMOT dataset, SAMIDARE achieves state-of-the-art performance, outperforming the baseline by 2.5 HOTA and 4.2 IDF1 points on the validation set. These results demonstrate that adaptive feature management using mask control and state-aware association provide a robust and efficient solution for dense sports tracking. Code is available at https://github.com/ZabuZabuZabu/SAMIDARE

Problem

• multi-object tracking
• dense scenes
• mask errors
• ID switches
• sports analysis

Innovation

• density-aware mask re-generation
• selective memory updates
• state-aware association
• tracking-by-segmentation
• multi-object tracking