Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame. To improve the robustness of VPR in perceptually aliasing scenarios, sequence-based VPR methods are proposed. These methods are either based on matching between frame sequences or extracting sequence descriptors for direct retrieval. However, the former is usually based on the assumption of constant velocity, which is difficult to hold in practice, and is computationally expensive and subject to sequence length. Although the latter overcomes these problems, existing sequence descriptors are constructed by aggregating features of multiple frames only, without interaction on temporal information, and thus cannot obtain descriptors with spatio-temporal discrimination. In this paper, we propose a sequence descriptor that effectively incorporates spatio-temporal information. Specifically, spatial attention within the same frame is utilized to learn spatial feature patterns, while attention in corresponding local regions of different frames is utilized to learn the persistence or change of features over time. We use a sliding window to control the temporal range of attention and use relative position encoding to construct sequential relationships between different features. This allows our descriptors to capture the intrinsic dynamics in a sequence of frames. Comprehensive experiments on challenging benchmark datasets show that the proposed approach outperforms recent state-of-the-art methods.

翻译：视觉地点识别（VPR）旨在从带有地理标签的图像数据库中检索与查询图像位于同一地点的帧。为提升VPR在感知混淆场景中的鲁棒性，提出了基于序列的VPR方法。这些方法要么基于帧序列间的匹配，要么通过提取序列描述符进行直接检索。然而，前者通常基于恒定速度假设，在实际中难以成立，且计算成本高且受序列长度限制。尽管后者克服了这些问题，但现有序列描述符仅通过聚合多帧特征构建，缺乏时间维度的信息交互，因此无法获得具有时空判别能力的描述符。本文提出一种有效融合时空信息的序列描述符。具体而言，利用同一帧内的空间注意力学习空间特征模式，同时利用不同帧对应局部区域的注意力学习特征随时间变化的持续性或变化性。我们使用滑动窗口控制注意力的时间范围，并采用相对位置编码构建不同特征间的序列关系。这使得我们的描述符能够捕获帧序列中的内在动态。在具有挑战性的基准数据集上的全面实验表明，所提方法优于现有最新技术。