AI summaryⓘ
The authors address the problem of locating ground-view videos by matching them to aerial images, which is usually done with full video data and fixed methods. They propose Progressive Cross-view Video Geo-localization (PCVG) to allow location tracking with partial or interrupted video input, more suitable for real-time use. Their method, X²Localizer, improves early and single-frame localization by aligning video frames with aerial images in a flexible way. They also introduce a strategy called Sliding-Window Re-Localization to update candidate locations dynamically without reprocessing the entire video. Experiments show their approach works well on full videos and is better at localizing with limited or intermittent data, making it more practical for real-world scenarios.
Cross-view Video Geo-localizationAerial Image RetrievalPrefix-based InferenceToken AggregationSliding-Window Re-LocalizationEarly LocalizationRandom-start EvaluationProgressive LocalizationRecall@K MetricsDynamic Observation Handling
Authors
Zichao Zeng, Weijia Fan, Yufan Chen, June Moh Goo, Junwei Zheng, Ruiping Liu, Kunyu Peng, Jiaming Zhang, Rainer Stiefelhagen, Jan Boehm
Abstract
Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images. However, CVG approaches rely on fixed-length inputs and post-hoc refinement, hindering online-oriented localization under partial or dynamic observations. In this work, we formulate Progressive Cross-view Video Geo-localization (PCVG) as a deployment-oriented extension and evaluation protocol of CVG, enabling localization under varying temporal budgets, prefix-based inference, random-start evaluation, and long-range localization with interruptions. To explore PCVG, we introduce X$^2$Localizer, a cross-grained alignment framework that jointly supervises global prefix-to-aerial retrieval and token-aggregated frame--aerial-tile matching with a budget-dependent asymmetric objective. Furthermore, we introduce a Sliding-Window Re-Localization (SWRL) strategy that dynamically refreshes candidate regions for failure recovery and long-range deployment without full-sequence reprocessing. Extensive experiments show that X$^2$Localizer preserves conventional full-video performance, with marginal gains of +0.1 Recall@1 and +0.3 Recall@10, while substantially improving early localization. In the challenging single-frame setting, X$^2$Localizer improves coarse retrieval by +4.7 Recall@1 and +11.5 Recall@10 over the previous state-of-the-art method. With SWRL, our approach further enables robust progressive localization under random-start and long-distance scenarios, narrowing the gap between benchmark evaluation and real-world deployment.