Parking Space Detection From Video By Augmenting Training Dataset

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.

• Type: Conference
• Year: 2009
• Where: ICIP
• Keywords: Auto Parking Techniques, ICIP 2009, Image Processing, Limited Training Dataset, Object Recognition