Event cameras, known for their high temporal resolution and ability to capture asynchronous changes, have gained significant attention for their potential in feature tracking, especially in challenging conditions. However, event cameras lack the fine-grained texture information that conventional cameras provide, leading to error accumulation in tracking. To address this, we propose a novel framework, BlinkTrack, which integrates event data with grayscale images for high-frequency feature tracking. Our method extends the traditional Kalman filter into a learning-based framework, utilizing differentiable Kalman filters in both event and image branches. This approach improves single-modality tracking and effectively solves the data association and fusion from asynchronous event and image data. We also introduce new synthetic and augmented datasets to better evaluate our model. Experimental results indicate that BlinkTrack significantly outperforms existing methods, exceeding 80 FPS with multi-modality data and 100 FPS with preprocessed event data. Codes and dataset are available at https://github.com/ColieShen/BlinkTrack.

Recent feature matching approaches like ETO have focused on developing lightweight matching algorithms for real-time applications. However, their lack of cross-image feature interaction and sufficient refinement often lead to a decline in matching accuracy. To address these challenges, we propose ETO+, a novel and accurate feature matching algorithm that incorporates a lightweight yet efficient bidirectional interaction module and multi-stage refinement. Specifically, we introduce Trans-CNN, a bidirectional feature interaction module that integrates CNN- and transformer-based techniques to enhance both intra-image feature refinement and inter-image feature fusion, all while maintaining a comparable computational cost. Furthermore, by leveraging the inherent sparsity of local feature matching, we propose an efficient strategy to adaptively reallocate computational resources within the network. Additionally, we design an adaptive loss function that mitigates the impact of large matching errors, thereby improving overall robustness. Extensive experiments on widely used datasets demonstrate that our approach achieves a strong balance between accuracy and computational efficiency. It outperforms ETO by 7.9 in AUC@5 on MegaDepth, respectively, while being about 40% faster than E-LoFTR.