Matches in SemOpenAlex for { <https://semopenalex.org/work/W4287802104> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W4287802104 abstract "According to several reports published by worldwide organisations, thousands of pedestrians die in road accidents every year. Due to this fact, vehicular technologies have been evolving with the intent of reducing these fatalities. This evolution has not finished yet since, for instance, the predictions of pedestrian paths could improve the current Automatic Emergency Braking Systems (AEBS). For this reason, this paper proposes a method to predict future pedestrian paths, poses and intentions up to 1s in advance. This method is based on Balanced Gaussian Process Dynamical Models (B-GPDMs), which reduce the 3D time-related information extracted from keypoints or joints placed along pedestrian bodies into low-dimensional spaces. The B-GPDM is also capable of inferring future latent positions and reconstruct their associated observations. However, learning a generic model for all kind of pedestrian activities normally provides less ccurate predictions. For this reason, the proposed method obtains multiple models of four types of activity, i.e. walking, stopping, starting and standing, and selects the most similar model to estimate future pedestrian states. This method detects starting activities 125ms after the gait initiation with an accuracy of 80% and recognises stopping intentions 58.33ms before the event with an accuracy of 70%. Concerning the path prediction, the mean error for stopping activities at a Time-To-Event (TTE) of 1s is 238.01mm and, for starting actions, the mean error at a TTE of 0s is 331.93mm." @default.
- W4287802104 created "2022-07-26" @default.
- W4287802104 creator A5002294104 @default.
- W4287802104 creator A5016004555 @default.
- W4287802104 creator A5023503494 @default.
- W4287802104 creator A5027189195 @default.
- W4287802104 date "2020-04-30" @default.
- W4287802104 modified "2023-09-23" @default.
- W4287802104 title "Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition" @default.
- W4287802104 doi "https://doi.org/10.48550/arxiv.2004.14747" @default.
- W4287802104 hasPublicationYear "2020" @default.
- W4287802104 type Work @default.
- W4287802104 citedByCount "0" @default.
- W4287802104 crossrefType "posted-content" @default.
- W4287802104 hasAuthorship W4287802104A5002294104 @default.
- W4287802104 hasAuthorship W4287802104A5016004555 @default.
- W4287802104 hasAuthorship W4287802104A5023503494 @default.
- W4287802104 hasAuthorship W4287802104A5027189195 @default.
- W4287802104 hasBestOaLocation W42878021041 @default.
- W4287802104 hasConcept C111919701 @default.
- W4287802104 hasConcept C119857082 @default.
- W4287802104 hasConcept C121332964 @default.
- W4287802104 hasConcept C121704057 @default.
- W4287802104 hasConcept C127413603 @default.
- W4287802104 hasConcept C154945302 @default.
- W4287802104 hasConcept C163716315 @default.
- W4287802104 hasConcept C199360897 @default.
- W4287802104 hasConcept C22212356 @default.
- W4287802104 hasConcept C2777113093 @default.
- W4287802104 hasConcept C2777735758 @default.
- W4287802104 hasConcept C2779662365 @default.
- W4287802104 hasConcept C38652104 @default.
- W4287802104 hasConcept C41008148 @default.
- W4287802104 hasConcept C61224824 @default.
- W4287802104 hasConcept C61326573 @default.
- W4287802104 hasConcept C62520636 @default.
- W4287802104 hasConcept C98045186 @default.
- W4287802104 hasConceptScore W4287802104C111919701 @default.
- W4287802104 hasConceptScore W4287802104C119857082 @default.
- W4287802104 hasConceptScore W4287802104C121332964 @default.
- W4287802104 hasConceptScore W4287802104C121704057 @default.
- W4287802104 hasConceptScore W4287802104C127413603 @default.
- W4287802104 hasConceptScore W4287802104C154945302 @default.
- W4287802104 hasConceptScore W4287802104C163716315 @default.
- W4287802104 hasConceptScore W4287802104C199360897 @default.
- W4287802104 hasConceptScore W4287802104C22212356 @default.
- W4287802104 hasConceptScore W4287802104C2777113093 @default.
- W4287802104 hasConceptScore W4287802104C2777735758 @default.
- W4287802104 hasConceptScore W4287802104C2779662365 @default.
- W4287802104 hasConceptScore W4287802104C38652104 @default.
- W4287802104 hasConceptScore W4287802104C41008148 @default.
- W4287802104 hasConceptScore W4287802104C61224824 @default.
- W4287802104 hasConceptScore W4287802104C61326573 @default.
- W4287802104 hasConceptScore W4287802104C62520636 @default.
- W4287802104 hasConceptScore W4287802104C98045186 @default.
- W4287802104 hasLocation W42878021041 @default.
- W4287802104 hasOpenAccess W4287802104 @default.
- W4287802104 hasPrimaryLocation W42878021041 @default.
- W4287802104 hasRelatedWork W1578916557 @default.
- W4287802104 hasRelatedWork W2013095503 @default.
- W4287802104 hasRelatedWork W2147823694 @default.
- W4287802104 hasRelatedWork W2783038087 @default.
- W4287802104 hasRelatedWork W2792612132 @default.
- W4287802104 hasRelatedWork W2897453949 @default.
- W4287802104 hasRelatedWork W3022601216 @default.
- W4287802104 hasRelatedWork W3162483426 @default.
- W4287802104 hasRelatedWork W4238439451 @default.
- W4287802104 hasRelatedWork W4324355716 @default.
- W4287802104 isParatext "false" @default.
- W4287802104 isRetracted "false" @default.
- W4287802104 workType "article" @default.