Browsing by Author "Chen, Tsuhan"

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A Bayesian hierarchical detection framework for parking space detection
(2008)
Huang, Ching-Chun
;
Wang, Sheng-Jyh
;
Chang, Yao-Jen
;
Chen, Tsuhan
;
電子工程學系及電子研究所
hi this paper, a 3-layer Bayesian hierarchical detection framework (BHDF) is proposed for robust parking space detection. In practice, the challenges of the parking space detection problem come from luminance variations, inter-occlusions among cars, and occlusions caused by environmental obstacles. Instead of determining the status of parking spaces one by one, the proposed BHDF framework models the inter-occluded patterns as semantic knowledge and couple local classifiers with adjacency constraints to determine the status of parking spaces in a row-by-row manner. By applying the BHDF to the parking space detection problem, the available parking spaces and the labeling of parked cars can be achieved in a robust and efficient manner. Furthermore, this BHDF framework is generic enough to be used for various kinds of detection and segmentation applications.
<bold>ROBUST PARKING SPACE DETECTION CONSIDERING INTER-SPACE CORRELATION</bold>
(2007)
Wu, Qi
;
Huang, Chingchun
;
Wang, Shih-yu
;
Chiu, Wei-Chen
;
Chen, Tsuhan
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.