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Multi-Query Person Retrieval (mQPRS) improves security by enabling the retrieval of multiple individuals in various scenarios while providing real-time situational awareness on edge devices. This paper introduces mQPRS using soft biometrics on the NVIDIA Jetson ORIN AGX platform for the first time. It employs YOLOv8s for person detection and proposes the DensePAR model for recognising person attributes, alongside a new ranking strategy. A low-rank approximation is applied to the DensePAR model to manage computational limits on edge devices. An ablation study assesses the effects of model compression on performance using the GPU and Nvidia Jetson Orin platforms. The system achieves a 74% true positive rate for multi-query person retrieval, processing at 20 frames per second with EfficientNet Backbone at float32 precision on the Nvidia Jetson Orin AGX.

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Multi-Query Person Retrieval on Edge Devices

  • 会议论文

  • 发表日期：

    2026年

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  • Jay Chaudhari,
  • Hetav Raval,
  • Hiren Galiyawala,
  • Paawan Sharma,
  • Mehul S. Raval

摘要

Multi-Query Person Retrieval (mQPRS) improves security by enabling the retrieval of multiple individuals in various scenarios while providing real-time situational awareness on edge devices. This paper introduces mQPRS using soft biometrics on the NVIDIA Jetson ORIN AGX platform for the first time. It employs YOLOv8s for person detection and proposes the DensePAR model for recognising person attributes, alongside a new ranking strategy. A low-rank approximation is applied to the DensePAR model to manage computational limits on edge devices. An ablation study assesses the effects of model compression on performance using the GPU and Nvidia Jetson Orin platforms. The system achieves a 74% true positive rate for multi-query person retrieval, processing at 20 frames per second with EfficientNet Backbone at float32 precision on the Nvidia Jetson Orin AGX.