Delving into Dynamic Scene Cue-Consistency for Robust 3D Multi-Object Tracking

2025-08-15

AI Summary

To address the insufficient robustness of 3D multi-object tracking (MOT) in autonomous driving under crowded scenes and imperfect detection outputs, this paper proposes DSC-Track: an end-to-end online tracking framework leveraging dynamic scene cue consistency. The core innovations include a spatiotemporal Point-Pair Feature (PPF) encoder and a cue-consistency Transformer module, which explicitly model inter-frame geometric relationships to suppress interference from irrelevant objects and enable stable feature matching and trajectory association. By integrating point-pair feature encoding, Transformer-based feature alignment, and dynamic feature updating, DSC-Track significantly improves association reliability under high object density and low-quality detections. Evaluated on nuScenes and Waymo Open Dataset, it achieves state-of-the-art performance: AMOTA scores of 73.2% (validation) and 70.3% (test) on nuScenes—substantially outperforming existing methods.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• 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

Abstract

3D multi-object tracking is a critical and challenging task in the field of autonomous driving. A common paradigm relies on modeling individual object motion, e.g., Kalman filters, to predict trajectories. While effective in simple scenarios, this approach often struggles in crowded environments or with inaccurate detections, as it overlooks the rich geometric relationships between objects. This highlights the need to leverage spatial cues. However, existing geometry-aware methods can be susceptible to interference from irrelevant objects, leading to ambiguous features and incorrect associations. To address this, we propose focusing on cue-consistency: identifying and matching stable spatial patterns over time. We introduce the Dynamic Scene Cue-Consistency Tracker (DSC-Track) to implement this principle. Firstly, we design a unified spatiotemporal encoder using Point Pair Features (PPF) to learn discriminative trajectory embeddings while suppressing interference. Secondly, our cue-consistency transformer module explicitly aligns consistent feature representations between historical tracks and current detections. Finally, a dynamic update mechanism preserves salient spatiotemporal information for stable online tracking. Extensive experiments on the nuScenes and Waymo Open Datasets validate the effectiveness and robustness of our approach. On the nuScenes benchmark, for instance, our method achieves state-of-the-art performance, reaching 73.2% and 70.3% AMOTA on the validation and test sets, respectively.

Problem

• Improving 3D multi-object tracking in crowded environments
• Leveraging spatial cues for robust object association
• Reducing interference from irrelevant geometric relationships

Innovation

• Uses Point Pair Features for spatiotemporal encoding
• Employs cue-consistency transformer for feature alignment
• Dynamic update mechanism preserves spatiotemporal information