资源高效的多视角感知：将语义掩码与掩码自编码器相结合
Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders
AI总结：
本文提出一种结合语义引导掩码与掩码自编码器的多视角感知方法，通过优先保留信息丰富区域，在维持检测跟踪精度的同时显著降低通信数据量。
摘要：
多视角系统已成为现代计算机视觉中的关键技术，在场景理解与分析方面提供了先进的能力。然而，这些系统在带宽限制和计算约束方面面临严峻挑战，尤其是对于无人机等资源受限的相机节点而言。本文提出了一种利用掩码自编码器（MAEs）实现通信高效的分布式多视角检测与跟踪的新方法。我们引入了一种语义引导的掩码策略，该策略利用预训练的分割模型和一个可调幂函数来优先处理信息丰富的图像区域。该方法与MAE相结合，在保留关键视觉信息的同时降低了通信开销。我们在虚拟和真实世界的多视角数据集上评估了我们的方法，结果表明，即使在高掩码率下，其在检测与跟踪性能指标方面也能达到与最先进技术相当的性能。我们的选择性掩码算法优于随机掩码，随着掩码率的增加，仍能保持更高的准确率和精确率。此外，与基线方法相比，我们的方法显著减少了传输数据量，从而在多视角跟踪性能与通信效率之间取得了平衡。
英文摘要：
Multiview systems have become a key technology in modern computer vision, offering advanced capabilities in scene understanding and analysis. However, these systems face critical challenges in bandwidth limitations and computational constraints, particularly for resource-limited camera nodes like drones. This paper presents a novel approach for communication-efficient distributed multiview detection and tracking using masked autoencoders (MAEs). We introduce a semantic-guided masking strategy that leverages pre-trained segmentation models and a tunable power function to prioritize informative image regions. This approach, combined with an MAE, reduces communication overhead while preserving essential visual information. We evaluate our method on both virtual and real-world multiview datasets, demonstrating comparable performance in terms of detection and tracking performance metrics compared to state-of-the-art techniques, even at high masking ratios. Our selective masking algorithm outperforms random masking, maintaining higher accuracy and precision as the masking ratio increases. Furthermore, our approach achieves a significant reduction in transmission data volume compared to baseline methods, thereby balancing multiview tracking performance with communication efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
