Reliability-Consistent Appearance Modeling for RGB-Infrared Multi-Object Tracking

Key points

• However, most existing methods compress RGB and infrared evidence into a unified descriptor.1
• We therefore propose RCA-MOT, a reliability-consistent appearance modeling framework that preserves cross-modal complementarity while retaining post-representation evidence controllability.1
• RGB-specific, infrared-specific, and fused identity representations are maintained as jointly accessible appearance states.1
• For each detection, a simple training-free target-scene estimator produces branch-wise appearance reliability used across the two temporal roles of appearance: relative reliability regulates different identity branches, absolute reliability calibrates borderline candidates near the ReID acceptance boundary, and the same branch-wise reliability controls how strongly matched descriptors update historical appearance memory.1

Sources (1)

• [1]Reliability-Consistent Appearance Modeling for RGB-Infrared Multi-Object Tracking
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 08:30 AM

  Recent RGB-infrared multi-object tracking (MOT) methods have substantially improved cross-modal representation learning and multimodal information integration.

  However, most existing methods compress RGB and infrared evidence into a unified descriptor.

Extractive summary: sentences quoted from the sources.