Abstract
This paper investigates the robustness of a new thermal-infrared pedestrian detection system under different outdoor environmental conditions. In first place the algorithm for pedestrian ROI extraction in thermal-infrared video based on both thermal and motion information is introduced. Then, the evaluation of the proposal is detailed after describing the complete thermal and motion information fusion. In this sense, the environment chosen for evaluation is described, and the twelve test sequences are specified. For each of the sequences captured from a forward-looking infrared FLIR A-320 camera, the paper explains the weather and light conditions under which it was captured. The results allow us to draw firm conclusions about the conditions under which it can be affirmed that it is efficient to use our thermal-infrared proposal to robustly extract human ROIs.
Highlights
The detection of pedestrians is a key application in the video surveillance domain [1]
This paper introduces a new algorithm for robust regions of interest (ROIs) extraction of pedestrians in thermal-infrared video based on the authors’ previous works [16,17]
This article has provided comprehensive information about tests that have been conducted to evaluate the performance of a new algorithm developed for detecting human in thermal-infrared video
Summary
The detection of pedestrians is a key application in the video surveillance domain [1]. The most intuitive idea when performing a pedestrian detection algorithm in the thermal-infrared spectrum is to take advantage of the fact that humans usually appear warmer than other objects in the scene [12,13]. This is not always the case [14]. A great amount of infrared images have low spatial resolution and lower sensitivity than visible spectrum images due to the technological limitations of thermal-infrared cameras. These defects often result in low image quality and a great amount of image noise
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