Head Pose Feature for Prediction Pedestrian Intention to Crossing the Road Using LSTM

Hanugra Aulia Sidharta, Muhammad Ilham Perdana, Eko Mulyanto Yuniarno, Berlian Al Kindhi, Mauridhi Hery Purnomo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Understanding pedestrian behaviour when crossing the road is an important key to the development of autonomous vehicles. Because pedestrians are considered Vul-nerable Road Users (VRUs), they are likely to be killed if they are involved in an accident. To ensure their safety, it is then necessary to predict the pedestrian's intention based on their behaviour. In this experiment, we propose head pose observation for predicting their intention, by observing pedestrians' head pose data, we can then predict their intention to cross the road. To achieve this purpose, we use human head detector and head pose extraction feature, and the resulting yaw, pitch and roll as three head pose features. To select the most optimal feature is important for predicting pedestrian intention, then we make 7 combination scenarios based on these three features and compare it with the same model. Based on this scenario, it is proved that all these three data are optimal to observe pedestrian intention. There are three behavioural annotation that have been used, there are crossing, not crossing and will crossing. We derive will crossing from the annotation looking and not crossing while waiting at the roadside. Prediction of pedestrian behaviour is done by using LSTM model, and the resulting precision on crossing and not crossing with 0.98, while will crossing is 0.94.

Original languageEnglish
Title of host publicationTENCON 2023 - 2023 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9798350302196
Publication statusPublished - 2023
Event38th IEEE Region 10 Conference, TENCON 2023 - Chiang Mai, Thailand
Duration: 31 Oct 20233 Nov 2023

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference38th IEEE Region 10 Conference, TENCON 2023
CityChiang Mai


  • Head pose features
  • LSTM
  • feature selection
  • pedestrian behavior
  • predicting pedestrian in-tention


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