1,424 research outputs found
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras
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.Comment: Accepted to IEEE ITSC 202
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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