This paper proposes a novel algorithm for detecting road scene objects (e.g., light poles, traffic signposts, and cars) from 3-D mobile-laser-scanning point cloud data for transportation-related applications. To describe local abstract features of point cloud objects, a contextual visual vocabulary is generated by integrating spatial contextual information of feature regions. Objects of interest are detected based on the similarity measures of the bag of contextual-visual words between the query object and the segmented semantic objects. Quantitative evaluations on two selected data sets show that the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.949, 0.970, 0.922, and 0.959, respectively, in detecting light poles, traffic signposts, and cars. Comparative studies demonstrate the superior performance of the proposed algorithm over other existing methods.
Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data
IEEE transactions on intelligent transportation systems ; 17 , 12 ; 3391-3406
2016
Aufsatz (Zeitschrift)
Englisch
Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data
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