In this paper, we address the challenging task of whole-body biometric
detection, recognition, and identification at distances of up to 500m and large
pitch angles of up to 50 degree. We propose an end-to-end system evaluated on
diverse datasets, including the challenging Biometric Recognition and
Identification at Range (BRIAR) dataset. Our approach involves pre-training the
detector on common image datasets and fine-tuning it on BRIAR's complex videos
and images. After detection, we extract body images and employ a feature
extractor for recognition. We conduct thorough evaluations under various
conditions, such as different ranges and angles in indoor, outdoor, and aerial
scenarios. Our method achieves an average F1 score of 98.29% at IoU = 0.7 and
demonstrates strong performance in recognition accuracy and true acceptance
rate at low false acceptance rates compared to existing models. On a test set
of 100 subjects with 444 distractors, our model achieves a rank-20 recognition
accuracy of 75.13% and a TAR@1%FAR of 54.09%.

我们提出了一个端到端系统，用于在长达 500 米和大于 50 度的高角度的情况下进行全身生物识别的检测、识别和辨认。我们的方法在各种室内、室外和航空场景下进行了全面的评估，表现出强大的识别准确性和低虚假接受率。

高空距离全身检测、识别和识别

Whole-body Detection, Recognition and Identification at Altitude and  Range

We present an imaging framework which converts three images from a gated
camera into high-resolution depth maps with depth accuracy comparable to pulsed
lidar measurements. Existing scanning lidar systems achieve low spatial
resolution at large ranges due to mechanically-limited angular sampling rates,
restricting scene understanding tasks to close-range clusters with dense
sampling. Moreover, today's pulsed lidar scanners suffer from high cost, power
consumption, large form-factors, and they fail in the presence of strong
backscatter. We depart from point scanning and demonstrate that it is possible
to turn a low-cost CMOS gated imager into a dense depth camera with at least
80m range - by learning depth from three gated images. The proposed
architecture exploits semantic context across gated slices, and is trained on a
synthetic discriminator loss without the need of dense depth labels. The
proposed replacement for scanning lidar systems is real-time, handles
back-scatter and provides dense depth at long ranges. We validate our approach
in simulation and on real-world data acquired over 4,000km driving in northern
Europe. Data and code are available at this https URL

通过利用带闸门相机的三幅图像，我们提出了一种成像框架，实现了与脉冲激光雷达测量相当的高精度深度图，有效地解决了现有扫描激光雷达系统的空间分辨率在大范围内降低的问题。