Collaborative perception in automated vehicles leverages the exchange of
information between agents, aiming to elevate perception results. Previous
camera-based collaborative 3D perception methods typically employ 3D bounding
boxes or bird's eye views as representations of the environment. However, these
approaches fall short in offering a comprehensive 3D environmental prediction.
To bridge this gap, we introduce the first method for collaborative 3D semantic
occupancy prediction. Particularly, it improves local 3D semantic occupancy
predictions by hybrid fusion of (i) semantic and occupancy task features, and
(ii) compressed orthogonal attention features shared between vehicles.
Additionally, due to the lack of a collaborative perception dataset designed
for semantic occupancy prediction, we augment a current collaborative
perception dataset to include 3D collaborative semantic occupancy labels for a
more robust evaluation. The experimental findings highlight that: (i) our
collaborative semantic occupancy predictions excel above the results from
single vehicles by over 30%, and (ii) models anchored on semantic occupancy
outpace state-of-the-art collaborative 3D detection techniques in subsequent
perception applications, showcasing enhanced accuracy and enriched
semantic-awareness in road environments.

介绍一种用于协同 3D 语义占据预测的方法，通过混合特征融合和车辆之间共享的压缩正交注意力特征来改进本地 3D 语义占据的预测，结果表明我们的协同语义占据预测在精确度和路面环境中的语义感知方面都优于单车的结果，并在后续感知应用中超越了最先进的协同 3D 检测技术。

连接自动化车辆中的混合特征融合协同语义占用预测

Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion  in Connected Automated Vehicles

Gait recognition is a promising biometric technology for identification due
to its non-invasiveness and long-distance. However, external variations such as
clothing changes and viewpoint differences pose significant challenges to gait
recognition. Silhouette-based methods preserve body shape but neglect internal
structure information, while skeleton-based methods preserve structure
information but omit appearance. To fully exploit the complementary nature of
the two modalities, a novel triple branch gait recognition framework, TriGait,
is proposed in this paper. It effectively integrates features from the skeleton
and silhouette data in a hybrid fusion manner, including a two-stream network
to extract static and motion features from appearance, a simple yet effective
module named JSA-TC to capture dependencies between all joints, and a third
branch for cross-modal learning by aligning and fusing low-level features of
two modalities. Experimental results demonstrate the superiority and
effectiveness of TriGait for gait recognition. The proposed method achieves a
mean rank-1 accuracy of 96.0% over all conditions on CASIA-B dataset and 94.3%
accuracy for CL, significantly outperforming all the state-of-the-art methods.
The source code will be available at this https URL

本文介绍了一种新的三支路人体步态识别框架（TriGait），它有效地融合了骨架和轮廓数据的特征，包括从外观提取静态和动态特征的两流网络，通过捕捉所有关节之间的依赖关系的简单而有效的 JSA-TC 模块，以及通过对两种模态的低级特征进行对齐和融合的第三支路用于交叉模态学习。实验证据表明 TriGait 在人体步态识别方面具有卓越的优越性和有效性。在 CASIA-B 数据集的所有条件下，所提方法的平均排名准确率为 96.0％，对 CL 数据集的准确率为 94.3％，明显优于所有现有技术方法。源代码将在此 URL 提供。