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Parking space detection from video by augmenting training dataset

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2 Author(s)
Wei Yu ; Carnegie Mellon University, USA ; Tsuhan Chen

Auto parking techniques are attracting more attention these days. In this paper, we develop an image-based method to estimate the depth contour in parking areas. Our algorithm is an extension of the canonical appearance-based models for object recognition. One challenge in object recognition is that limited training dataset can hardly represent all kinds intra-class and inter-class variations. We propose to augment the limited training dataset by on-the-spot learning from test data. The information is obtained by applying a fast block based stereo algorithm to estimate a rough disparity map. New ¿soft¿ samples are created to augment the training sample library. We present improved classification performance by using the proposed technique.

Published in:

2009 16th IEEE International Conference on Image Processing (ICIP)

Date of Conference:

7-10 Nov. 2009