Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approach
This addresses the challenge of identifying persons across ground and aerial platforms, which is incremental as it builds on existing video ReID methods with a new dataset and adaptation approach.
The authors tackled the problem of cross-platform video person re-identification by constructing the first large-scale Ground-to-Aerial video dataset (G2A-VReID) with 185,907 images and 5,576 tracklets, and proposed a method using CLIP and adapters that achieved superior results on existing and new datasets.
In this paper, we construct a large-scale benchmark dataset for Ground-to-Aerial Video-based person Re-Identification, named G2A-VReID, which comprises 185,907 images and 5,576 tracklets, featuring 2,788 distinct identities. To our knowledge, this is the first dataset for video ReID under Ground-to-Aerial scenarios. G2A-VReID dataset has the following characteristics: 1) Drastic view changes; 2) Large number of annotated identities; 3) Rich outdoor scenarios; 4) Huge difference in resolution. Additionally, we propose a new benchmark approach for cross-platform ReID by transforming the cross-platform visual alignment problem into visual-semantic alignment through vision-language model (i.e., CLIP) and applying a parameter-efficient Video Set-Level-Adapter module to adapt image-based foundation model to video ReID tasks, termed VSLA-CLIP. Besides, to further reduce the great discrepancy across the platforms, we also devise the platform-bridge prompts for efficient visual feature alignment. Extensive experiments demonstrate the superiority of the proposed method on all existing video ReID datasets and our proposed G2A-VReID dataset.