Intelligent transportation systems (ITS) have revolutionized modern road
infrastructure, providing essential functionalities such as traffic monitoring,
road safety assessment, congestion reduction, and law enforcement. Effective
vehicle detection and accurate vehicle pose estimation are crucial for ITS,
particularly using monocular cameras installed on the road infrastructure. One
fundamental challenge in vision-based vehicle monitoring is keypoint detection,
which involves identifying and localizing specific points on vehicles (such as
headlights, wheels, taillights, etc.). However, this task is complicated by
vehicle model and shape variations, occlusion, weather, and lighting
conditions. Furthermore, existing traffic perception datasets for keypoint
detection predominantly focus on frontal views from ego vehicle-mounted
sensors, limiting their usability in traffic monitoring. To address these
issues, we propose SKoPe3D, a unique synthetic vehicle keypoint dataset
generated using the CARLA simulator from a roadside perspective. This
comprehensive dataset includes generated images with bounding boxes, tracking
IDs, and 33 keypoints for each vehicle. Spanning over 25k images across 28
scenes, SKoPe3D contains over 150k vehicle instances and 4.9 million keypoints.
To demonstrate its utility, we trained a keypoint R-CNN model on our dataset as
a baseline and conducted a thorough evaluation. Our experiments highlight the
dataset's applicability and the potential for knowledge transfer between
synthetic and real-world data. By leveraging the SKoPe3D dataset, researchers
and practitioners can overcome the limitations of existing datasets, enabling
advancements in vehicle keypoint detection for ITS.

智能交通系统革新了现代道路基础设施，车辆检测、车辆姿态估计、关键点检测以及 SKoPe3D 数据集的应用有望推动智能交通领域的发展。

SKoPe3D: 交通监控摄像机中的车辆关键点感知的合成数据集

SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from  Traffic Monitoring Cameras

We consider a category-level perception problem, where one is given 3D sensor
data picturing an object of a given category (e.g. a car), and has to
reconstruct the pose and shape of the object despite intra-class variability
(i.e. different car models have different shapes). We consider an active shape
model, where -- for an object category -- we are given a library of potential
CAD models describing objects in that category, and we adopt a standard
formulation where pose and shape estimation are formulated as a non-convex
optimization. Our first contribution is to provide the first certifiably
optimal solver for pose and shape estimation. In particular, we show that
rotation estimation can be decoupled from the estimation of the object
translation and shape, and we demonstrate that (i) the optimal object rotation
can be computed via a tight (small-size) semidefinite relaxation, and (ii) the
translation and shape parameters can be computed in closed-form given the
rotation. Our second contribution is to add an outlier rejection layer to our
solver, hence making it robust to a large number of misdetections. Towards this
goal, we wrap our optimal solver in a robust estimation scheme based on
graduated non-convexity. To further enhance robustness to outliers, we also
develop the first graph-theoretic formulation to prune outliers in
category-level perception, which removes outliers via convex hull and maximum
clique computations; the resulting approach is robust to 70%-90% outliers. Our
third contribution is an extensive experimental evaluation. Besides providing
an ablation study on a simulated dataset and on the PASCAL3D+ dataset, we
combine our solver with a deep-learned keypoint detector, and show that the
resulting approach improves over the state of the art in vehicle pose
estimation in the ApolloScape datasets.

本文提出了一个主要解决物体类别感知问题的非凸优化模型，其中包括针对模型内部变异性的解决方案，将图形学和优化算法相结合，以提高汽车姿态估计等领域的精度和鲁棒性。