Gradient Compressive Sensing for Image Data Reduction in UAV Based Search and Rescue in the Wild

Abstract

Search and rescue operations usually require significant resources, personnel, equipment, and time. In order to optimize the resources and expenses and to increase the efficiency of operations, the use of unmanned aerial vehicles (UAVs) and aerial photography is considered for fast reconnaissance of large and unreachable terrains. The images are then transmitted to control center for automatic processing and pattern recognition. Furthermore, due to the limited transmission capacities and significant battery consumption for recording high resolution images, in this paper we consider the use of smart acquisition strategy with decreased amount of image pixels following the compressive sensing paradigm. The images are completely reconstructed in the control center prior to the application of image processing for suspicious objects detection. The efficiency of this combined approach depends on the amount of acquired data and also on the complexity of the scenery observed. The proposed approach is tested on various high resolution aerial images, while the achieved results are analyzed using different quality metrics and validation tests. Additionally, a user study is performed on the original images to provide the baseline object detection performance.

Visual-Based Person Detection for Search-and-Rescue with UAS: Humans vs. Machine Learning Algorithm

“…This approach resulted in only a minor loss of accuracy. In [23], the authors used the aforementioned detection model to conduct performance comparisons of the system on compressive-sensing-reconstructed images and original images, focusing primarily on image quality and information exchange. In [24], the authors tried different approaches, applying and analyzing various salient detection algorithms to detect lost persons.…”

Multimodel Deep Learning for Person Detection in Aerial Images

“…The first problem is that the product of recording is a large number of high resolution images that need to be transmitted to the control center for processing. To address this problem, Musić et al used the compressive sensing algorithm in order to decrease the amount of image data [22] for transmission and also reconstructed the initial image for further processing using mean shift clustering. The second problem is that the objects of interest (person) in those images are limited in pixels and resolution.…”

A Distributed Compressed Sensing-based Algorithm for the Joint Recovery of Signal Ensemble

“…In wireless sensor networks, sensors have limited computation capability and energy resources without assistance of an y established infrastructures, so many studies in the literature are conducted considering these limitations for various applications [6][7][8]. In [9], influences of compressive sensing parameters in compression of a common set of artificial signal on nodes' lifetime is partially discussed.…”