Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In this paper, we propose a new aerial video dataset and benchmark for low altitude UAV target tracking, as well as, a photo-realistic UAV simulator that can be coupled with tracking methods. Our benchmark provides the first evaluation of many state-of-the-art and popular trackers on 123 new and fully annotated HD video sequences captured from a low-altitude aerial perspective. Among the compared trackers, we determine which ones are the most suitable for UAV tracking both in terms of tracking accuracy and run-time. The simulator can be used to evaluate tracking algorithms in real-time scenarios before they are deployed on a UAV “in the field”, as well as, generate synthetic but photo-realistic tracking datasets with automatic ground truth annotations to easily extend existing real-world datasets. Both the benchmark and simulator are made publicly available to the vision community on our website to further research in the area of object tracking from UAVs. (https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx.).

/pdf/a-benchmark-and-simulator-for-uav-tracking-1uj73gzc8j.pdf

A Benchmark and Simulator for UAV Tracking

The days where swarms of unmanned aerial vehicles (UAVs) will occupy our skies are fast approaching due to the introduction of cost-efficient and reliable small aerial vehicles and the increasing demand for use of such vehicles in a plethora of civil applications. Governments and industry alike have been heavily investing in the development of UAVs. As such it is important to understand the characteristics of networks with UAVs to enable the incorporation of multiple, coordinated aerial vehicles into the air traffic in a reliable and safe manner. To this end, this survey reports the characteristics and requirements of UAV networks for envisioned civil applications over the period 2000–2015 from a communications and networking viewpoint. We survey and quantify quality-of-service requirements, network-relevant mission parameters, data requirements, and the minimum data to be transmitted over the network. Furthermore, we elaborate on general networking related requirements such as connectivity, adaptability, safety, privacy, security, and scalability. We also report experimental results from many projects and investigate the suitability of existing communication technologies for supporting reliable aerial networking.

Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint

This article presents a vision for future unmanned aerial vehicles (UAV)-assisted disaster management, considering the holistic functions of disaster prediction, assessment, and response. Here, UAVs not only survey the affected area but also assist in establishing vital wireless communication links between the survivors and nearest available cellular infrastructure. A perspective of different classes of geophysical, climate-induced, and meteorological disasters based on the extent of interaction between the UAV and terrestrially deployed wireless sensors is presented in this work, with suitable network architectures designed for each of these cases. The authors outline unique research challenges and possible solutions for maintaining connected aerial meshes for handoff between UAVs and for systems-specific, security- and energy-related issues. This article is part of a special issue on drones.

Help from the Sky: Leveraging UAVs for Disaster Management

The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the “new” aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.

Unmanned Aerial Vehicle for Remote Sensing Applications—A Review

A generic and robust approach for the real-time detection of people and vehicles from an Unmanned Aerial Vehicle
(UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance and
surveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trained
cascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approach
for people detection in thermal imagery based on a similar cascaded classification technique combining additional
multivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people under
varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection.
Performance of the detector is optimized to reduce the overall false positive rate by aiming at the detection of each object
of interest (vehicle/person) at least once in the environment (i.e. per search patter flight path) rather than every object in
each image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic object
detection rate for each flight pattern exceeds 90%.

/pdf/real-time-people-and-vehicle-detection-from-uav-imagery-4fid2p6ywp.pdf

Real-time people and vehicle detection from UAV imagery

Computed Tomography (CT) based baggage security screening systems are of increasing use in transportation security. The ability to automatically identify potential threat item is a key aspect of current research in this area. Here we present a comparison of varying classification approaches for the automated detection of threat objects in cluttered 3D CT imagery from such security screening systems. By combining 3D medical image segmentation techniques with 3D shape classification and retrieval methods we compare five varying final classification stage approaches and present significant performance achievements in the automated detection of specified exemplar items.

/pdf/a-comparison-of-classification-approaches-for-threat-3bhkyvayxc.pdf

A comparison of classification approaches for threat detection in CT based baggage screening

Generalised wide are search and surveillance is a common-place tasking for multi-sensory equipped autonomous systems. Here we present on a key supporting topic to this task - the automatic interpretation, fusion and detected target reporting from multi-modal sensor information received from multiple autonomous platforms deployed for wide-area environment search. We detail the realization of a real-time methodology for the automated detection of people and vehicles using combined visible-band (EO), thermal-band (IR) and radar sensing from a deployed network of multiple autonomous platforms (ground and aerial). This facilities real-time target detection, reported with varying levels of confidence, using information from both multiple sensors and multiple sensor platforms to provide environment-wide situational awareness. A range of automatic classification approaches are proposed, driven by underlying machine learning techniques, that facilitate the automatic detection of either target type with cross-modal target confirmation. Extended results are presented that show both the detection of people and vehicles under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance evaluation is presented at an episodic level with individual classifiers optimized for maximal each object of interest (vehicle/person) detection over a given search path/pattern of the environment, across all sensors and modalities, rather than on a per sensor sample basis. Episodic target detection, evaluated over a number of wide-area environment search and reporting tasks, generally exceeds 90%+ for the targets considered here.

/pdf/multi-modal-target-detection-for-autonomous-wide-area-search-r1udrt8hr4.pdf

Multi-modal target detection for autonomous wide area search and surveillance

We address the challenge of human behaviour analysis within automated image understanding. Whilst prior work concentrates on this task within visible-band (EO) imagery, by contrast we target basic human pose classification in thermal-band (infrared, IR) imagery. By leveraging the key advantages of limb localization this imagery offers we target two distinct human pose classification problems of varying complexity: 1) identifying passive or active individuals within the scene and 2) the identification of individuals potentially carrying weapons. Both approaches use a discrete set of features capturing body pose characteristics from which a range of machine learning techniques are then employed for final classification. Significant success is shown on these challenging tasks over a wide range of environmental conditions within the wider context of automated human target tracking in thermal-band (IR) imagery.

/pdf/human-pose-classification-within-the-context-of-near-ir-2h4lzb387n.pdf

Human pose classification within the context of near-IR imagery tracking

Abstract The recretohalophyte Karelinia caspia is of forage and medical value and can remediate saline soils. We here assess the contribution of primary/secondary metabolism to osmotic adjustment and ROS homeostasis in Karelinia caspia under salt stress using multi‐omic approaches. Computerized phenomic assessments, tests for cellular osmotic changes and lipid peroxidation indicated that salt treatment had no detectable physical effect on K. caspia. Metabolomic analysis indicated that amino acids, saccharides, organic acids, polyamine, phenolic acids, and vitamins accumulated significantly with salt treatment. Transcriptomic assessment identified differentially expressed genes closely linked to the changes in above primary/secondary metabolites under salt stress. In particular, shifts in carbohydrate metabolism (TCA cycle, starch and sucrose metabolism, glycolysis) as well as arginine and proline metabolism were observed to maintain a low osmotic potential. Chlorogenic acid/vitamin E biosynthesis was also enhanced, which would aid in ROS scavenging in the response of K. caspia to salt. Overall, our findings define key changes in primary/secondary metabolism that are coordinated to modulate the osmotic balance and ROS homeostasis to contribute to the salt tolerance of K. caspia.

/pdf/defining-key-metabolic-roles-in-osmotic-adjustment-and-ros-2h1y1l7u.pdf

Defining key metabolic roles in osmotic adjustment and ROS homeostasis in the recretohalophyte Karelinia caspia under salt stress

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