Real‐time surveillance detection system for medium‐altitude long‐endurance unmanned aerial vehicles

Abstract

Summary The detection of ambiguous objects, although challenging, is of great importance for any surveillance system and especially for an unmanned aerial vehicle, where the measurements are affected by the great observing distance. Wildfire outbursts and illegal migration are only some of the examples that such a system should distinguish and report to the appropriate authorities. More specifically, Southern European countries commonly suffer from those problems due to the mountainous terrain and thick forests that contain. Unmanned aerial vehicles like the “Hellenic Civil Unmanned Air Vehicle” project have been designed to address high‐altitude detection tasks and patrol the borders and woodlands for any ambiguous activity. In this paper, a moment‐based blob detection approach is proposed that uses the thermal footprint obtained from single infrared images and distinguishes human‐ or fire‐sized and shaped figures. Our method is specifically designed so as to be appropriately integrated into hardware acceleration devices, such as General Purpose Computation on Graphics Processing Units (GPGPUs) and field programmable gate arrays, and takes full advantage of their respective parallelization capabilities succeeding real‐time performances and energy efficiency. The timing evaluation of the proposed hardware accelerated algorithm's adaptations shows an achieved speedup of up to 7 times, as compared to a highly optimized CPU‐only based version.

Multisized Object Detection Using Spaceborne Optical Imagery

“…[34]. A surveillance detection system [35] has been proposed for various fire detection, and search and rescue (SAR) operation by using thermal information retrieved from infrared images human vs large fire patches.…”

Toward a robot swarm protecting a group of migrants

“…More than 2400 acquisitions were realized to create a dataset that will train the network. With these raw data describing the four (4) situations selected previously, we calculated some features and kept nine (9) of them that we can use to differentiate the different states. These features are shown in Table 3 (where A x,i , A y,i , A z,i are measurements taken in the axis of accelerometer, and G x,i , G y,i , G z,i are measurements of the gyroscope; i is the sample of the window).…”

“…This paper presents only the optimal way-points and robot faults management algorithm, including the rover control. For solving migrants tracking, some existing solutions could be implemented, such as using fixedwing unmanned aerial vehicle (UAV), such as suggested in [9][10][11]; however, this subject is out of the scope of this research work.…”

Detection of bodies in maritime rescue operations using unmanned aerial vehicles with multispectral cameras

“…There are some previous works addressing people detection on land or in indoor environments with traditional machine learning methods such as Andriluka et al (). For example, Amanatiadis, Bampis, Karakasis, Gasteratos, and Sirakoulis () used Chevyshev image moments and thresholding techniques, Niedzielski, Jurecka, Stec, et al () used color information and a ‐means method, Avola et al () implemented an red‐green‐blue local binary pattern (RGB‐LBP) descriptor, Blondel et al () and Rudol and Doherty () combined local visual features such as histogram of gradients (HOG) to be used as input of a cascaded Haar classifier, or combined with an support vector machines (SVM) by Portmann et al (), speeded up robust features (SURF) and fast library for approximate nearest neighbors (FLANN) by Symington, Waharte, Julier, and Trigoni () and local image texture was used by Coulter et al ().…”