AGM: Adaptive Graph Enhanced Graph Matching for Planar Tracking

In recent years, graph matching has made substantial progress in planar object tracking, offering end-to-end learning capabilities. However, these methods often fall short in real-world tracking tasks due to their reliance on static, manually constructed graphs that fail to effectively account for the dynamic distribution of feature points and their relationships within individual video frames. To address these challenges, we introduce the adaptive graph matching (AGM) Network, a resilient object tracking model designed to handle complex transformations and changes in object appearance with significant accuracy. The cornerstone of AGM lies in its innovative construction of dynamic graphs that model the feature points across video frames. Nodes represent key feature points in the graph, while edges adaptively form based on the spatial relationships and descriptors of these points. This allows the graph to adjust flexibly to the properties and relationships inherent in the feature points of each frame, enhancing its fidelity to the actual structure and changes of the planar object across the video sequence. AGM employs self-attention mechanisms to guide the adaptive graph construction, and cross-attention mechanisms to capture the relationships between adjacency matrices and their associated features. Our ablation study shows that these self and cross-attention mechanisms play an integral role in enhancing the model’s tracking performance, providing a more robust solution to real-world complications such as motion blur and object occlusion. We trained AGM and validated its performance on three datasets. The results show that AGM consistently outperforms other state-of-the-art models, demonstrating the effectiveness of our approach.