SCATR: Mitigating New Instance Suppression in LiDAR-based Tracking-by-Attention via Second Chance Assignment and Track Query Dropout

2026-03-02

AI Summary

This work addresses the performance gap between LiDAR-based Tracking-by-Attention (TBA) and detection-based tracking (TBD) paradigms, which stems primarily from TBA’s high miss rate. To mitigate the inherent conflict between detection and tracking tasks in TBA and improve robustness to newly appearing or previously missed objects, we propose SCATR, a novel framework incorporating two architecture-agnostic training strategies: Second Chance Assignment and Track Query Dropout. These mechanisms effectively enhance the model’s ability to recover missed detections and maintain consistent trajectories. Evaluated on the nuScenes benchmark, SCATR achieves a state-of-the-art AMOTA of 7.6% absolute improvement over prior LiDAR-based TBA methods, substantially narrowing the long-standing performance gap with TBD approaches.

Technology Category

• Computer Vision: Motion & Tracking
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

LiDAR-based tracking-by-attention (TBA) frameworks inherently suffer from high false negative errors, leading to a significant performance gap compared to traditional LiDAR-based tracking-by-detection (TBD) methods. This paper introduces SCATR, a novel LiDAR-based TBA model designed to address this fundamental challenge systematically. SCATR leverages recent progress in vision-based tracking and incorporates targeted training strategies specifically adapted for LiDAR. Our work's core innovations are two architecture-agnostic training strategies for TBA methods: Second Chance Assignment and Track Query Dropout. Second Chance Assignment is a novel ground truth assignment that concatenates unassigned track queries to the proposal queries before bipartite matching, giving these track queries a second chance to be assigned to a ground truth object and effectively mitigating the conflict between detection and tracking tasks inherent in tracking-by-attention. Track Query Dropout is a training method that diversifies supervised object query configurations to efficiently train the decoder to handle different track query sets, enhancing robustness to missing or newborn tracks. Experiments on the nuScenes tracking benchmark demonstrate that SCATR achieves state-of-the-art performance among LiDAR-based TBA methods, outperforming previous works by 7.6\% AMOTA and successfully bridging the long-standing performance gap between LiDAR-based TBA and TBD methods. Ablation studies further validate the effectiveness and generalization of Second Chance Assignment and Track Query Dropout. Code can be found at the following link: \href{https://github.com/TRAILab/SCATR}{https://github.com/TRAILab/SCATR}

Problem

• LiDAR-based tracking
• Tracking-by-Attention
• false negative errors
• new instance suppression
• performance gap

Innovation

• Second Chance Assignment
• Track Query Dropout
• LiDAR-based Tracking-by-Attention
• AMOTA
• nuScenes