Vehicle semantics extraction and retrieval for long-term carpark video surveillance

Clarence Weihan Cheong*, Ryan Woei-Sheng Lim, John See, Lai Kuan Wong, Ian K. T. Tan, Azrin Aris

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Car park video surveillance data provides plenty of semantic rich data such as vehicle color, trajectory, speed, and type which can be tapped into and extracted for video and data analytics. We present methods for extracting and retrieving color and motion semantics from long term carpark video surveillance. This is a challenging task in outdoor scenarios due to ever-changing illumination and weather conditions, while retrieval time also increases as data size grows. To address these challenges, we subdivided the search space into smaller chunks by introducing spatio-temporal cubes or atoms, which can store and retrieve these semantics at ease. The proposed method was tested on 2, days of continuous data from an outdoor carpark under various lighting and weather conditions. We report the precision, recall and F1 scores to determine the overall performance of the system.

Original languageEnglish
Title of host publicationMultiMedia Modeling. MMM 2018
Number of pages12
ISBN (Electronic)9783319736006
ISBN (Print)9783319735993
Publication statusPublished - 13 Jan 2018
Event24th International Conference on MultiMedia Modeling 2018 - Bangkok, Thailand
Duration: 5 Feb 20187 Feb 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on MultiMedia Modeling 2018
Abbreviated titleMMM 2018


  • Carpark surveillance
  • Retrieval systems
  • Vehicle semantic extraction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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