Learning Appearance and Motion Cues for Panoptic Tracking

2025-03-12

AI Summary

This work addresses the challenge of robust pixel-level spatiotemporal scene understanding for robotic manipulation in dynamic environments. To mitigate tracking failures caused by severe occlusion and non-rigid deformation, we propose a novel video panoptic instance tracking framework that jointly models appearance and motion cues. Our method introduces a two-stage fusion module: first establishing initial inter-frame instance associations via cross-frame matching, then refining correspondences using motion-augmented appearance embeddings. We further incorporate multi-scale deformable convolutions, semantic-guided motion offset estimation, and a dedicated dual-branch network head for joint panoptic segmentation and tracking. Evaluated on VIPER and KITTI-STEP—two leading benchmarks for video panoptic tracking—our approach achieves state-of-the-art performance, particularly excelling in object identity preservation, with substantial gains over prior methods.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Panoptic tracking enables pixel-level scene interpretation of videos by integrating instance tracking in panoptic segmentation. This provides robots with a spatio-temporal understanding of the environment, an essential attribute for their operation in dynamic environments. In this paper, we propose a novel approach for panoptic tracking that simultaneously captures general semantic information and instance-specific appearance and motion features. Unlike existing methods that overlook dynamic scene attributes, our approach leverages both appearance and motion cues through dedicated network heads. These interconnected heads employ multi-scale deformable convolutions that reason about scene motion offsets with semantic context and motion-enhanced appearance features to learn tracking embeddings. Furthermore, we introduce a novel two-step fusion module that integrates the outputs from both heads by first matching instances from the current time step with propagated instances from previous time steps and subsequently refines associations using motion-enhanced appearance embeddings, improving robustness in challenging scenarios. Extensive evaluations of our proposed etname model on two benchmark datasets demonstrate that it achieves state-of-the-art performance in panoptic tracking accuracy, surpassing prior methods in maintaining object identities over time. To facilitate future research, we make the code available at http://panoptictracking.cs.uni-freiburg.de

Problem

• Integrates instance tracking in panoptic segmentation for pixel-level video interpretation.
• Leverages appearance and motion cues to improve dynamic scene understanding.
• Introduces a two-step fusion module for robust instance association in tracking.

Innovation

• Uses multi-scale deformable convolutions for motion
• Integrates appearance and motion cues via network heads
• Introduces two-step fusion for robust instance matching