Open AccessReal‐time transmission tower detection from video based on a feature descriptor
Author(s) -
Cerón Alexander,
Mondragón Iván,
Prieto Flavio
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0477
Subject(s) - computer science , artificial intelligence , object detection , computer vision , support vector machine , transmission tower , transmission (telecommunications) , feature extraction , feature (linguistics) , pattern recognition (psychology) , ground truth , tower , grid , video tracking , process (computing) , object (grammar) , mathematics , geography , telecommunications , linguistics , philosophy , geometry , archaeology , operating system
In this study, the authors propose a new method for transmission tower detection that involves the use of visual features and the linear content of the scene. For this process, they developed a descriptor based on a grid of two‐dimensional feature descriptors that is useful not only for object detection, but also for tracking the area of interest. For the detection and classification, they used a support vector machine. The experiments were conducted with a dataset of real world images from transmission tower videos that were used to validate the strategy by comparing it with the ground truth. The results showed that the obtained method is fast and appropriate for tower detection in video sequences of environments that include rural and urban areas. The detection took less than 50 ms and was faster than other methods.