Vision sensors are versatile and can capture a wide range of visual cues,
such as color, texture, shape, and depth. This versatility, along with the
relatively inexpensive availability of machine vision cameras, played an
important role in adopting vision-based environment perception systems in
autonomous vehicles (AVs). However, vision-based perception systems can be
easily affected by glare in the presence of a bright source of light, such as
the sun or the headlights of the oncoming vehicle at night or simply by light
reflecting off snow or ice-covered surfaces; scenarios encountered frequently
during driving. In this paper, we investigate various glare reduction
techniques, including the proposed saturated pixel-aware glare reduction
technique for improved performance of the computer vision (CV) tasks employed
by the perception layer of AVs. We evaluate these glare reduction methods based
on various performance metrics of the CV algorithms used by the perception
layer. Specifically, we considered object detection, object recognition, object
tracking, depth estimation, and lane detection which are crucial for autonomous
driving. The experimental findings validate the efficacy of the proposed glare
reduction approach, showcasing enhanced performance across diverse perception
tasks and remarkable resilience against varying levels of glare.

本研究探讨了各种减少眩光的技术，包括针对自动驾驶车辆感知层所采用的计算机视觉任务的饱和像素感知眩光减少技术，通过基于各种性能指标评估表明该方法提高了计算机视觉的性能，对不同程度的眩光表现出出色的韧性。

处理自动驾驶车辆的眩光问题

How to deal with glare for improved perception of Autonomous Vehicles

Current gait recognition research mainly focuses on identifying pedestrians
captured by the same type of sensor, neglecting the fact that individuals may
be captured by different sensors in order to adapt to various environments. A
more practical approach should involve cross-modality matching across different
sensors. Hence, this paper focuses on investigating the problem of
cross-modality gait recognition, with the objective of accurately identifying
pedestrians across diverse vision sensors. We present CrossGait inspired by the
feature alignment strategy, capable of cross retrieving diverse data
modalities. Specifically, we investigate the cross-modality recognition task by
initially extracting features within each modality and subsequently aligning
these features across modalities. To further enhance the cross-modality
performance, we propose a Prototypical Modality-shared Attention Module that
learns modality-shared features from two modality-specific features.
Additionally, we design a Cross-modality Feature Adapter that transforms the
learned modality-specific features into a unified feature space. Extensive
experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of
CrossGait: (1) it exhibits promising cross-modality ability in retrieving
pedestrians across various modalities from different sensors in diverse scenes,
and (2) CrossGait not only learns modality-shared features for cross-modality
gait recognition but also maintains modality-specific features for
single-modality recognition.

本研究主要关注于穿越不同传感器跨模态识别行人的步态识别研究，通过提出 CrossGait 方法，利用特征对齐策略进行跨模态数据检索，并通过 Prototypical Modality-shared Attention Module 和 Cross-modality Feature Adapter 进一步增强模态之间的识别性能。在 SUSTech1K 数据集上广泛实验，CrossGait 在多样的场景中展现了良好的跨模态能力，能准确检索不同传感器和多种模态的行人，同时保持了单模态识别所需的模态特定特征。

跨模态步态识别：连接 LiDAR 和相机模态进行人类识别

Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities  for Human Identification

Novel vision sensors such as thermal, hyperspectral, polarization, and event
cameras provide information that is not available from conventional intensity
cameras. An obstacle to using these sensors with current powerful deep neural
networks is the lack of large labeled training datasets. This paper proposes a
Network Grafting Algorithm (NGA), where a new front end network driven by
unconventional visual inputs replaces the front end network of a pretrained
deep network that processes intensity frames. The self-supervised training uses
only synchronously-recorded intensity frames and novel sensor data to maximize
feature similarity between the pretrained network and the grafted network. We
show that the enhanced grafted network reaches competitive average precision
(AP50) scores to the pretrained network on an object detection task using
thermal and event camera datasets, with no increase in inference costs.
Particularly, the grafted network driven by thermal frames showed a relative
improvement of 49.11% over the use of intensity frames. The grafted front end
has only 5--8% of the total parameters and can be trained in a few hours on a
single GPU equivalent to 5% of the time that would be needed to train the
entire object detector from labeled data. NGA allows new vision sensors to
capitalize on previously pretrained powerful deep models, saving on training
cost and widening a range of applications for novel sensors.

该文提出了一种基于 Network Grafting Algorithm 的自监督训练算法，用于使用冷热图像信息进行优化前的深度学习神经网络挑战。提出的算法取得了在物体检测任务上与传统深度学习网络相当的表现，同时在训练时缩短了时间和参数量。