Robust Parking Space Detection Considering Inter-Space Correlation

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.

• Type: Conference
• Year: 2007
• Where: ICMCS
• Authors: Qi Wu, Chingchun Huang, Shih-yu Wang, Wei-chen Chiu, Tsuhan Chen
• Keywords: ICMCS 2007, Markov Random Field, Parking Space, Support Vector Machine