1. Introduction
Mirror detection is a critical computer vision task with applications ranging from autonomous driving to augmented reality, yet it faces challenges due to mirrors' diverse appearances and reflective properties within dynamic video scenes. Traditional methods often struggle with robustness and scalability in complex environments. This paper introduces MirrorMamba to overcome these limitations by providing a robust and efficient solution for mirror detection in videos. The primary model utilized in this article is the novel MirrorMamba framework, incorporating a Mamba-based architecture for sequence modeling.
2. Related Work
Prior research in mirror detection has explored various approaches, including semantic segmentation, depth estimation cues, and handcrafted features, often limited by their reliance on static image analysis or local context. Recent advancements in deep learning, particularly recurrent neural networks and transformers, have shown promise in video understanding, but often incur high computational costs for real-time applications. This work positions MirrorMamba against these methods, highlighting its efficiency and ability to handle temporal dependencies effectively while maintaining high accuracy in diverse video settings.
3. Methodology
The MirrorMamba framework integrates a specialized Mamba block into a convolutional backbone, allowing for efficient processing of both spatial features and long-range temporal dependencies across video frames. Input video frames are processed through an encoder-decoder structure where Mamba layers are strategically placed to capture global context and temporal dynamics. A multi-scale feature fusion module enhances the representation power, combining information from different resolutions. The model is trained using a composite loss function, incorporating both segmentation loss and temporal consistency loss, to ensure accurate and stable mirror predictions over time.
4. Experimental Results
Experiments were conducted on several public and proprietary mirror detection datasets, evaluating MirrorMamba against state-of-the-art methods in terms of F-measure, IoU, and inference speed. MirrorMamba consistently achieved higher accuracy and demonstrated superior performance in real-time scenarios, particularly on challenging video sequences with dynamic lighting and occlusions. The results indicate significant improvements in both detection quality and computational efficiency, validating the effectiveness of the Mamba-based architecture for this task. Below is a representative summary of performance metrics, showcasing MirrorMamba's competitive edge over other methods. MirrorMamba notably outperforms prior art in F-measure and IoU while maintaining a high frame rate, proving its utility for real-world applications requiring efficient and accurate mirror detection.
5. Discussion
The superior performance of MirrorMamba underscores the potential of state-space models like Mamba in addressing complex video understanding tasks, particularly those requiring robust temporal modeling. The framework's efficiency makes it suitable for deployment in real-time applications where computational resources are often constrained. Future work could explore extending MirrorMamba to detect other reflective surfaces or integrating it with multi-modal inputs to further enhance robustness across even more challenging environments and expand its applicability.