Matches in SemOpenAlex for { <https://semopenalex.org/work/W3098932128> ?p ?o ?g. }
Showing items 1 to 73 of
73
with 100 items per page.
- W3098932128 abstract "A human driver can gauge the intention and signals given by other road users indicative of their future behaviour. The intentions and signals are identified by looking at the cues originating from vulnerable road users or their surroundings (hand signals, head orientation, posture, traffic signals, distance to curb, etc.). Taking all these cues into account by creating a separate detector for each is an extremely difficult task. Instead, this MSc Thesis will explore the possibility of using a generic contextual cue in optical flow originating from a pedestrian with deep learning methods to improve the path prediction in a naturalistic driving scenario. The contribution of this work is to examine multiple ways to extract relevant information from the optical flow and also explore the possibility of using the entire the high-dimensional optical flow using convolutions and soft-attention to help identify relevant pixels for the prediction task. This work elaborates on the extraction and processing of optical flow features. It proposes 2 Recurrent neural networks (RNN) based model: one to work with the histogram of optical flow features and the other one to take in the dense optical flow directly. Also, visualization of the soft-attention weights is done to add a step that helps in the interpretability of the RNN model incorporating dense optical flow. From the experimental results, optical flow features have shown significant improvements in terms of predicting probabilistic confidence for tracks with some changes in their motion mode. It was seen that the convolution-attention RNN model was able to work with dense optical flow features and position of pedestrians as input to obtain better results among all the combinations of features and models compared in this work." @default.
- W3098932128 created "2020-11-23" @default.
- W3098932128 creator A5091140964 @default.
- W3098932128 date "2020-01-01" @default.
- W3098932128 modified "2023-09-26" @default.
- W3098932128 title "Predicting pedestrian path using optical flow as context cue" @default.
- W3098932128 hasPublicationYear "2020" @default.
- W3098932128 type Work @default.
- W3098932128 sameAs 3098932128 @default.
- W3098932128 citedByCount "0" @default.
- W3098932128 crossrefType "journal-article" @default.
- W3098932128 hasAuthorship W3098932128A5091140964 @default.
- W3098932128 hasConcept C115961682 @default.
- W3098932128 hasConcept C119857082 @default.
- W3098932128 hasConcept C127413603 @default.
- W3098932128 hasConcept C151730666 @default.
- W3098932128 hasConcept C153180895 @default.
- W3098932128 hasConcept C154945302 @default.
- W3098932128 hasConcept C155542232 @default.
- W3098932128 hasConcept C201995342 @default.
- W3098932128 hasConcept C2779343474 @default.
- W3098932128 hasConcept C2780451532 @default.
- W3098932128 hasConcept C2781067378 @default.
- W3098932128 hasConcept C31972630 @default.
- W3098932128 hasConcept C41008148 @default.
- W3098932128 hasConcept C49937458 @default.
- W3098932128 hasConcept C53533937 @default.
- W3098932128 hasConcept C81363708 @default.
- W3098932128 hasConcept C86803240 @default.
- W3098932128 hasConceptScore W3098932128C115961682 @default.
- W3098932128 hasConceptScore W3098932128C119857082 @default.
- W3098932128 hasConceptScore W3098932128C127413603 @default.
- W3098932128 hasConceptScore W3098932128C151730666 @default.
- W3098932128 hasConceptScore W3098932128C153180895 @default.
- W3098932128 hasConceptScore W3098932128C154945302 @default.
- W3098932128 hasConceptScore W3098932128C155542232 @default.
- W3098932128 hasConceptScore W3098932128C201995342 @default.
- W3098932128 hasConceptScore W3098932128C2779343474 @default.
- W3098932128 hasConceptScore W3098932128C2780451532 @default.
- W3098932128 hasConceptScore W3098932128C2781067378 @default.
- W3098932128 hasConceptScore W3098932128C31972630 @default.
- W3098932128 hasConceptScore W3098932128C41008148 @default.
- W3098932128 hasConceptScore W3098932128C49937458 @default.
- W3098932128 hasConceptScore W3098932128C53533937 @default.
- W3098932128 hasConceptScore W3098932128C81363708 @default.
- W3098932128 hasConceptScore W3098932128C86803240 @default.
- W3098932128 hasLocation W30989321281 @default.
- W3098932128 hasOpenAccess W3098932128 @default.
- W3098932128 hasPrimaryLocation W30989321281 @default.
- W3098932128 hasRelatedWork W1539703227 @default.
- W3098932128 hasRelatedWork W2753614144 @default.
- W3098932128 hasRelatedWork W2807452521 @default.
- W3098932128 hasRelatedWork W2810931617 @default.
- W3098932128 hasRelatedWork W2901064116 @default.
- W3098932128 hasRelatedWork W2921426834 @default.
- W3098932128 hasRelatedWork W2922179433 @default.
- W3098932128 hasRelatedWork W2965110031 @default.
- W3098932128 hasRelatedWork W2969360776 @default.
- W3098932128 hasRelatedWork W2978397934 @default.
- W3098932128 hasRelatedWork W2981355328 @default.
- W3098932128 hasRelatedWork W3024540363 @default.
- W3098932128 hasRelatedWork W3043306352 @default.
- W3098932128 hasRelatedWork W3100100339 @default.
- W3098932128 hasRelatedWork W3113416056 @default.
- W3098932128 hasRelatedWork W3126161938 @default.
- W3098932128 hasRelatedWork W3129037456 @default.
- W3098932128 hasRelatedWork W3157832040 @default.
- W3098932128 hasRelatedWork W3178277268 @default.
- W3098932128 hasRelatedWork W3153192071 @default.
- W3098932128 isParatext "false" @default.
- W3098932128 isRetracted "false" @default.
- W3098932128 magId "3098932128" @default.
- W3098932128 workType "article" @default.