Pedestrian group detection in shared space

Hao Cheng, Yao Li, Monika Sester

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

20 Citations (Scopus)


In shared space, pedestrians are often found walking in groups and behaving differently than individual pedestrians. However, automatically detecting pedestrian groups with high accuracy is not trivial given the dynamic environment and interactions in mixed traffic. Instead of tedious manual work and in order to cope with large scales of data, we propose a time-sequence Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for pedestrian group detection. It is based on coexisting time and Euclidean distance between pedestrians. Our approach outputs reliable results with high IoU values. It can be easily adapted to other groups, e.g., cyclists and animals. In addition to individual behavior, the output data with differentiation of group behavior can be used in further studies in intent detection and motion prediction.
Original languageEnglish
Title of host publication2019 IEEE Intelligent Vehicles Symposium (IV)
Place of PublicationPiscataway, NJ
Number of pages8
ISBN (Electronic)978-1-7281-0560-4
ISBN (Print)978-1-7281-0561-1, 978-1-7281-0559-8 (USB)
Publication statusPublished - 2019
Externally publishedYes
Event2019 IEEE Intelligent Vehicles Symposium, IV 2019 - MINES ParisTech, Paris, France
Duration: 9 Jun 201912 Jun 2019


Conference2019 IEEE Intelligent Vehicles Symposium, IV 2019
Abbreviated titleIV 2019


  • ITC-CV
  • n/a OA procedure


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