Expanding on the BRIAR Dataset: A Comprehensive Whole Body Biometric Recognition Resource at Extreme Distances and Real-World Scenarios (Collections 1-4)

• URL: http://arxiv.org/abs/2501.14070v2
• Date: Tue, 04 Feb 2025 19:48:06 GMT
• Title: Expanding on the BRIAR Dataset: A Comprehensive Whole Body Biometric Recognition Resource at Extreme Distances and Real-World Scenarios (Collections 1-4)
• Authors: Gavin Jager, David Cornett III, Gavin Glenn, Deniz Aykac, Christi Johnson, Robert Zhang, Ryan Shivers, David Bolme, Laura Davies, Scott Dolvin, Nell Barber, Joel Brogan, Nick Burchfield, Carl Dukes, Andrew Duncan, Regina Ferrell, Austin Garrett, Jim Goddard, Jairus Hines, Bart Murphy, Sean Pharris, Brandon Stockwell, Leanne Thompson, Matthew Yohe,
• Abstract summary: The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years. The technology still suffers greatly when applied to non-conventional settings such as those seen when performing identification at extreme distances or from elevated cameras on buildings or mounted to UAVs. This paper summarizes an extension to the largest dataset currently focused on addressing these operational challenges.
• Score: 1.5267473555334419
• License:
• Abstract: The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years providing high accuracy and robustness in more challenging collection environments and consumer applications. However, the technology still suffers greatly when applied to non-conventional settings such as those seen when performing identification at extreme distances or from elevated cameras on buildings or mounted to UAVs. This paper summarizes an extension to the largest dataset currently focused on addressing these operational challenges, and describes its composition as well as methodologies of collection, curation, and annotation.

Related papers

• Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions [11.557368031775717]
  This paper evaluates research solutions for identifying individuals at ranges and altitudes. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification.
  arXiv  Detail & Related papers  (2024-09-03T02:17:36Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• Deep Learning-Based Object Pose Estimation: A Comprehensive Survey [73.74933379151419]
  We discuss the recent advances in deep learning-based object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks.
  arXiv  Detail & Related papers  (2024-05-13T14:44:22Z)
• FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude [67.55994773068191]
  This paper presents the end-to-end design, development and evaluation of FarSight. FarSight is an innovative software system designed for whole-body (fusion of face, gait and body shape) biometric recognition. We test FarSight's effectiveness using the newly acquired IARPA Biometric Recognition and Identification at Altitude and Range dataset.
  arXiv  Detail & Related papers  (2023-06-29T16:14:27Z)
• Evaluation of Environmental Conditions on Object Detection using Oriented Bounding Boxes for AR Applications [7.022872089444935]
  Scene analysis and object recognition play a crucial role in augmented reality (AR) New approach is proposed that involves using oriented bounding boxes with a detection and recognition deep network to improve performance and processing time. Results indicate that the proposed approach tends to produce better Average Precision and greater accuracy for small objects in most of the tested conditions.
  arXiv  Detail & Related papers  (2023-06-29T09:17:58Z)
• Persistent Homology Meets Object Unity: Object Recognition in Clutter [2.356908851188234]
  Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots. We propose a new descriptor, TOPS, for point clouds generated from depth images and an accompanying recognition framework, THOR, inspired by human reasoning. THOR outperforms state-of-the-art methods on both the datasets and achieves substantially higher recognition accuracy for all the scenarios of the UW-IS Occluded dataset.
  arXiv  Detail & Related papers  (2023-05-05T19:42:39Z)
• Modeling Entities as Semantic Points for Visual Information Extraction in the Wild [55.91783742370978]
  We propose an alternative approach to precisely and robustly extract key information from document images. We explicitly model entities as semantic points, i.e., center points of entities are enriched with semantic information describing the attributes and relationships of different entities. The proposed method can achieve significantly enhanced performance on entity labeling and linking, compared with previous state-of-the-art models.
  arXiv  Detail & Related papers  (2023-03-23T08:21:16Z)
• Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset [1.965868228519436]
  Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, have limited utility in more advanced security, forensics, and military applications. This paper describes methods used to collect and curate the dataset, and the dataset's characteristics at the current stage.
  arXiv  Detail & Related papers  (2022-11-03T15:51:39Z)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)
• Scalable Vehicle Re-Identification via Self-Supervision [66.2562538902156]
  Vehicle Re-Identification is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. We propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time.
  arXiv  Detail & Related papers  (2022-05-16T12:14:42Z)
• Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance [3.8015092217142223]
  We show that a holistic attention network based super-resolution approach and a custom-built altitude data exploitation network can increase the detection efficacy in real-world settings. We evaluate the system on two public, large aerial-capture animal datasets, SAVMAP and AED.
  arXiv  Detail & Related papers  (2021-11-12T17:30:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.