<bold>ROBUST PARKING SPACE DETECTION CONSIDERING INTER-SPACE CORRELATION</bold>

被引：0

作者：

Wu, Qi ^{[1
]}

Huang, Chingchun ^{[2
]}

Wang, Shih-yu ^{[3
]}

Chiu, Wei-Chen ^{[3
]}

Chen, Tsuhan ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA

[2] Ind Technol Res Inst, Hsinchu, Taiwan

[3] Natl Chiao Tung Univ, Hsinchu 30050, Taiwan

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-5 | 2007年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：659 / +

页数：2

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