Robust parking space detection considering inter-space correlation

Qi Wu*, Ching-Chun Huang, Shih Yu Wang, Wei-Chen Chiu, Tsuhan Chen

*Corresponding author for this work

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

86 Scopus citations

Abstract

A major problem in metropolitan areas is searching for parking spaces. In this paper, we propose a novel method for parking space detection. Given input video captured by a camera, we can distinguish the empty spaces from the occupied spaces by using an 8-class Support Vector Machine (SVM) classifier with probabilistic outputs. Considering the inter-space correlation, the outputs of the SVM classifier are fused together using a Markov Random Field (MRF) framework. The result is much improved detection performance, even when there are significant occlusion and shadowing effects in the scene. Experimental results are given to show the robustness of the proposed approach.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
Pages659-662
Number of pages4
StatePublished - 1 Dec 2007
EventIEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China
Duration: 2 Jul 20075 Jul 2007

Publication series

NameProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

Conference

ConferenceIEEE International Conference onMultimedia and Expo, ICME 2007
CountryChina
CityBeijing
Period2/07/075/07/07

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