Unconstrained Body Recognition at Altitude and Range: Comparing Four Approaches

• URL: http://arxiv.org/abs/2502.07130v1
• Date: Mon, 10 Feb 2025 23:49:06 GMT
• Title: Unconstrained Body Recognition at Altitude and Range: Comparing Four Approaches
• Authors: Blake A Myers, Matthew Q Hill, Veda Nandan Gandi, Thomas M Metz, Alice J O'Toole,
• Abstract summary: We focus on learning persistent body shape characteristics that remain stable over time. We introduce a body identification model based on a Vision Transformer (ViT) and on a Swin-ViT model. All models are trained on a large and diverse dataset of over 1.9 million images of approximately 5k identities across 9 databases.
• Score: 0.0
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• Abstract: This study presents an investigation of four distinct approaches to long-term person identification using body shape. Unlike short-term re-identification systems that rely on temporary features (e.g., clothing), we focus on learning persistent body shape characteristics that remain stable over time. We introduce a body identification model based on a Vision Transformer (ViT) (Body Identification from Diverse Datasets, BIDDS) and on a Swin-ViT model (Swin-BIDDS). We also expand on previous approaches based on the Linguistic and Non-linguistic Core ResNet Identity Models (LCRIM and NLCRIM), but with improved training. All models are trained on a large and diverse dataset of over 1.9 million images of approximately 5k identities across 9 databases. Performance was evaluated on standard re-identification benchmark datasets (MARS, MSMT17, Outdoor Gait, DeepChange) and on an unconstrained dataset that includes images at a distance (from close-range to 1000m), at altitude (from an unmanned aerial vehicle, UAV), and with clothing change. A comparative analysis across these models provides insights into how different backbone architectures and input image sizes impact long-term body identification performance across real-world conditions.

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