Motion-Appearance Synergistic Dual-Branch Joint Decision for Airborne Infrared Multi-Object Tracking
Airborne infrared small-object tracking is crucial for applications such as autonomous reconnaissance and border surveillance. Unlike visible-light imagery, infrared data provides stable imaging in low-light and hazy conditions. However, tracking in this domain is exceptionally challenging due to the diminutive size of targets (approximately $13\times 23$ pixels), their weak textural features, and high susceptibility to complex background interference, including heat sources and sensor noise. Existing re-identification (ReID) models, predominantly designed for visible-light data, struggle to extract robust and discriminative features from such impoverished inputs. To address these limitations, we introduce a dual-branch joint decision tracking framework that synergistically integrates motion prediction with hierarchical appearance representation. The framework’s core is a hierarchical dual-branch ReID model: its shallow branch extracts stable intensity and structural features for short-term consistency, while its deep branch captures high-level semantic features for robust re-identification after occlusion. Furthermore, a novel time-window-based motion prediction module enhances trajectory accuracy by aggregating and statistically weighting multi-frame kinematic data. Crucially, we develop an adaptive joint decision mechanism that dynamically fuses these motion and hierarchical appearance cues. This mechanism constructs a unified cost matrix by scaling motion distances based on scene-level velocity statistics and selectively employs deep features to resolve ambiguities. Evaluated on a public infrared tracking dataset, our method demonstrates robust overall tracking performance, achieving a Multiple Object Tracking Accuracy (MOTA) of 95.6% and an Identification F1 (IDF1) score of 97.7%. These results underscore the framework’s enhanced stability and robustness in complex, dynamic environments, offering a more reliable solution for airborne infrared multi-object tracking than existing approaches.