UNDER CONSTRUCTION ...
PEDESTRIAN BEHAVIOR/INTENTION MODELING FOR AUTONOMOUS DRIVING
•Looking at Humans in the Age of Self-driving and Highly Automated Vehicles
•Understanding Pedestrian Behavior in Traffic Scenes
•Are They Going To Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior
•Context-based Pedestrian Path Prediction
•Pedestrian Dynamics from a Vehicle Perspective
•Intention-aware Pedestrian Avoidance
•Activity Forecasting
•Pedestrian Crossing Prediction using Multiple Context-based Models
•Pedestrian Cross? A Study on Pedestrian Path Prediction
•Goal-directed Pedestrian Prediction
•Forecasting Interactive Dynamics of Pedestrians With Fictitious Play
•Learning and Predicting On-road Pedestrian Behavior Around Vehicles
•Pedestrian Prediction By Planning Using Deep NNs
•Set-based Prediction of Pedestrians in Urban Environments Considering Formalized Traffic Rules
•Data-driven Approach for Pedestrian Intention Estimation
Appendix: Human Pose Estimation
•DeepPose: Human Pose Estimation via Deep NNs
•Heterogeneous Multi-Learning for Pose Estimation
•A Multi-source Deep Model for Pose Estimation
•Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
•DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
•Stacked Hourglass Networks for Pose Estimation
•Associative Embedding: End-to-end Learning for Joint Detection and Grouping
•Real-time Multi-person 2D Pose Estimation using Part Affinity Fields
•VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
•ArtTrack: Articulated Multi-person Tracking in Wild
•Towards Multi-person Pose Estimation in Wild
•DensePose: Dense Human Pose Estimation in Wild
•RMPE: Regional Multi-person Pose Estimation