Deep Learning for Vision-based Prediction: A Survey
원문 초록
Vision-based prediction algorithms have a wide range of applications including autonomous driving, surveillance, human-robot interaction, weather prediction. The objective of this paper is to provide an overview of the field in the past five years with a particular focus on deep learning approaches. For this purpose, we categorize these algorithms into video prediction, action prediction, trajectory prediction, body motion prediction, and other prediction applications. For each category, we highlight the common architectures, training methods and types of data used. In addition, we discuss the common evaluation metrics and datasets used for vision-based prediction tasks. A database of all the information presented in this survey including, cross-referenced according to papers, datasets and metrics, can be found online at https://github.com/aras62/vision-based-prediction.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
TrajFlow: Multi-modal Motion Prediction via Flow Matching
DSTIGCN: Deformable Spatial-Temporal Interaction Graph Convolution Network for Pedestrian Trajectory Prediction
GRIP: Graph-based Interaction-aware Trajectory Prediction
A Survey on Vision-Language-Action Models for Autonomous Driving
Epona: Autoregressive Diffusion World Model for Autonomous Driving
토론
이 논문과 연결된 포럼 글이 아직 없습니다.