Human Detection Based on Infrared Images in Forestry Environments

Abstract

Abstract. It is essential to have a reliable system to detect humans in close range of forestry machines to stop cutting or carrying operations to prohibit any harm to humans. Due to the lighting conditions and high occlusion from the vegetation, human detection using RGB cameras is difficult. This paper introduces two human detection methods in forestry environments using a thermal camera; one shape-dependent and one shape-independent approach. Our segmentation algorithm estimates location of the human by extracting vertical and horizontal borders of regions of interest (ROIs). Based on segmentation results, features such as ratio of height to width and location of the hottest spot are extracted for the shape-dependent method. For the shape-independent method all extracted ROI are resized to the same size, then the pixel values (temperatures) are used as a set of features. The features from both methods are fed into different classifiers and the results are evaluated using side-accuracy and side-efficiency. The results show that by using shapeindependent features, based on three consecutive frames, we reach a precision rate of 80% and recall of 76%.

Emergency Support Unmanned Aerial Vehicle for Forest Fire Surveillance

“…The work in [11] presented vision based algorithms for the detection and classification of crops with images captured with a monocular camera installed in a UAV and, although not directly related to fire detection, a useful tool for the analysis of the rural environment in which air vehicles are working. Another work that collected methods of image processing with applicability in this area was presented by [12], in which, through the analysis and processing of images captured by a thermal camera, the detection of people in forest areas was carried out, which is a tool of great interest for the cooperative work between air vehicles and operational personnel on the ground.…”

Advances in using robots in forestry operations

“…3) (e.g. Brunner and Gizachew, 2014;Hauglin et al, 2017;Oveland et al, 2018;Wells, 2018), obstacles (Lideskog, 2018;Puliti et al, 2018), people (Ostovar et al, 2016) and what effects the machines have on the terrain (Marra et al, 2018;Melander and Ritala, 2018;Salmivaara et al, 2018). There is a huge variation in the methods used, with typical techniques including computer vision via two-dimensional (2D) and three-dimensional (3D) terrestrial laser scanners (Talbot et al, 2017).…”

Implementation of a System for Real-Time Detection and Localization of Terrain Objects on Harvested Forest Land

“…Imaging sensors can be deployed on vehicles or forest machines for object detection or to assist operations. Some studies have fully demonstrated their potential for application, such as color camera systems for tree detection and distance measurement [10], stereo camera systems for scanning and reconstructing land previously processed by mounders [11], human detection in forest environments using a thermal camera [12], structured light cameras used for log recognition [13], and fusion of multiple imaging sensors to achieve more complex functions such as modeling of forest nonstructural roads [14]. The application of imaging sensors can improve the environmental observation ability of the forest machine system and is a good assistant to the visual observation of the operator in the operation cabin.…”