Abstract
This paper proposes an efficient object localization method based on a vanishing line. The proposed method can be much improved in time efficiency since it requires scanning only vanishing line area. It requires the time complexity of O(n) while the existing sliding window method requires the time complexity O(n2) for detecting all objects in the entire image. In addition, the range of detection area can be also remarkably reduced when compared with the sliding window method. As a result, the total range and times for searching in the proposed method can be significantly reduced by considering together the distance and position of the object. The experiment on the proposed method is performed with the virtual road data set known as SYNTHIA, and the competitive results are obtained.
Highlights
The importance of vision-based semantic segmentation in urban scenarios has emerged due to the result of deep learning-based research on autonomous vehicle
This paper proposes an efficient object localization method based on a vanishing line
Localization Methods Depending on Distance In vanishing line-based object localization method (VLOL), the pixels of an image are first scanned along a vanishing line
Summary
The importance of vision-based semantic segmentation in urban scenarios has emerged due to the result of deep learning-based research on autonomous vehicle. The deep learning-based object recognition can be deployed after the localization process of estimating the location of objects in an image is completely achieved. In road images for autonomous vehicle, the first step in recognizing an object such as a person, a car, traffic light, or an extra obstacle is a localization method for determining existence of an object by using a bounding box. This process is a binary classification stage that predicts only whether an object exists or not in the specific location without recognizing the information about an object. Until now most previous works [1]-[10] on localization have been carried out based on identifying space boundaries by bounding boxes, which contain all regions of an image to recognize the extent of an object
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