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Real-Time Detection of Pedestrians: A Comparison of Three Segmentation Algorithms in Infrared Video
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Author(s): Juan Serrano-Cuerda (Universidad de Castilla – La Mancha, Spain), José Carlos Castillo (Universidad Carlos III de Madrid, Spain), María T. López (Universidad de Castilla – La Mancha, Spain)and Antonio Fernández-Caballero (Universidad de Castilla – La Mancha, Spain)
Copyright: 2016
Pages: 19
Source title:
Interdisciplinary Perspectives on Contemporary Conflict Resolution
Source Author(s)/Editor(s): Paulo Novais (University of Minho, Portugal)and Davide Carneiro (University of Minho, Portugal)
DOI: 10.4018/978-1-5225-0245-6.ch013
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Abstract
Real-time pedestrian detection is a key technology for video surveillance. A widespread approach for detecting pedestrians is the use of color information. In recent times, the use of thermal infrared cameras has revealed to be an excellent alternative that offers good results in people segmentation. Nonetheless, thermal infrared cameras are very sensitive to the overall heat detected at each image. Moreover, a great amount of infrared images has low spatial resolution and lower sensitivity than visible spectrum images due to the technological limitations of infrared cameras. This chapter introduces a comparison of three different algorithms for real-time and robust pedestrian detection in the infrared spectrum. The aim of the paper is to look for the best algorithms prepared to resolve the conflicts that arise in the detection process in image sequences. We propose to use simple rules as conflict resolution mechanism when the outputs of the three algorithms do not coincide.
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