D3SSTrack: Center-Focused State-Space Modeling for Monocular 3D Multi-Object Tracking

Monocular 3D multi-object tracking (3D MOT) remains challenging because it is hard to model how objects move over time and to keep correct identities without explicit depth information. In this context, we introduce D3SSTrack, a novel tracking-by-detection framework that integrates Mamba state-space modeling into the 3D tracking pipeline. At its core is the Solid State Track (SST) block, which extends the original Mamba block with dropout regularization and an additional projection layer to improve feature integration before temporal fusion. This design enables efficient modeling of long-range temporal dependencies while maintaining real-time performance at 38 FPS on a single GPU. The proposed tracker combines structured sequence modeling with effective temporal association, improving robustness against occlusions and abrupt motion changes. On the KITTI benchmark, D3SSTrack achieves the best sAMOTA (97.12%) and AMOTA (49.95%) among recent monocular 3D MOT methods, outperforming the best model S3MOT by 0.16% and 0.22%, respectively. Our results highlight the potential of state space-based architectures for real-world monocular 3D MOT applications.