IIIT Hyderabad Publications |
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Visual Localization in Highly Crowded Urban EnvironmentsAuthors: Abdul Hafeez,Manpreet Singh Arora,K. Madhava Krishna, C V Jawahar Conference: IROS 2013 Date: 2013-11-03 Report no: IIIT/TR/2013/125 AbstractVisual localization in crowded dynamic environ- ments requires information about static and dynamic objects. This paper presents a robust method that learns the useful features from multiple runs in highly crowded urban environ- ments. Useful features are identified as distinctive ones that are also reliable to extract in diverse imaging conditions. Relative importance of features is used to derive the weight for each feature. The popular Bag-of-words model is used for image retrieval and localization, where query image is the current view of the environment and database contains the visual experience from previous runs. Based on the reliability, features are augmented and eliminated over runs. This reduces the size of representation, and makes it more reliable in crowded scenes. We tested the proposed method on data sets collected from highly crowded Indian urban outdoor settings. Experiments have shown that with the help of a small subset (10%) of the detected features, we can reliably localize the camera. We achieve superior results in terms of localization accuracy even when more than 90% of the pixels are occluded or dynamic. Full paper: pdf Centre for Visual Information Technology |
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