Motion intention optimization for multirotor robust video stabilization

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper we present an optimization algorithm for simultaneously detecting video freeze and obtaining the minimum number of the frame required in motion intention estimation for real time robust video stabilization on multirotor unmanned aerial vehicles. A combination of a filter and a threshold is used to the video freeze detection, and for optimizing the algorithm, we find the minimum number of frames for motion intention estimation without decrease the performance.Peer ReviewedPostprint (author's final draft

Object Detection and Tracking in Wide Area Surveillance Using Thermal Imagery

The main objective behind this thesis is to examine how existing vision-based detection and tracking algorithms perform in thermal imagery-based video surveillance. While color-based surveillance has been extensively studied, these techniques can not be used during low illumination, at night, or with lighting changes and shadows which limits their applicability. The main contributions in this thesis are (1) the creation of a new color-thermal dataset, (2) a detailed performance comparison of different color-based detection and tracking algorithms on thermal data and (3) the proposal of an adaptive neural network for false detection rejection. Since there are not many publicly available datasets for thermal-video surveillance, a new UNLV Thermal Color Pedestrian Dataset was collected to evaluate the performance of popular color-based detection and tracking in thermal images. The dataset provides an overhead view of humans walking through a courtyard and is appropriate for aerial surveillance scenarios such as unmanned aerial systems (UAS). Three popular detection schemes are studied for thermal pedestrian detection: 1) Haar-like features, 2) local binary pattern (LBP) and 3) background subtraction motion detection. A i) Kalman filter predictor and ii) optical flow are used for tracking. Results show that combining Haar and LBP detections with a 50% overlap rule and tracking using Kalman filters can improve the true positive rate (TPR) of detection by 20%. However, motion-based methods are better at rejecting false positive in non-moving camera scenarios. The Kalman filter with LBP detection is the most efficient tracker but optical flow better rejects false noise detections. This thesis also presents a technique for learning and characterizing pedestrian detections with heat maps and an object-centric motion compensation method for UAS. Finally, an adaptive method to reject false detections using error back propagation using a neural network. The adaptive rejection scheme is able to successfully learn to identify static false detections for improved detection performance

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed

Pedestrian Detection Using Unmanned Aerial Vehicles

V tejto práci boli porovnávané metódy detekcie chodcov na snímkoch z dronov pomocou konvolučných neurónových sietí. Boli použité dve detekčné siete - YOLOv5 a Retinanet. U týchto sietí bola porovnávaná ich presnosť, rýchlosť a náročnosť trénovania. Taktiež bol sledovaný vplyv niektorých parametrov trénovania na výsledky detekcie. Pre trénovanie a testovanie bol použitý Stanford Drone Dataset ktorý obsahuje videozáznamy zachytené s dronom z kampusu Univerzity v Stanforde.The main focus of this study was pedestrian detection using drones and convolutional neural networks. 2 detection networks were used - YOLOv5 and Retinanet. The performance was compared based on precision and speed of detection and the demands on training process. Impact of certian training parameters on results was also observed. For training and testing Stanford Drone Dataset was used, containing video recordings captured by drones at Stanford University campus.460 - Katedra informatikyvýborn

SeaVipers - Computer Vision and Inertial Position Reference Sensor System (CVIPRSS)

This work describes the design and development of an optical, Computer Vision (CV) based sensor for use as a Position Reference System (PRS) in Dynamic Positioning (DP). Using a combination of robotics and CV techniques, the sensor provides range and heading information to a selected reference object. The proposed optical system is superior to existing ones because it does not depend upon special reflectors nor does it require a lengthy set-up time. This system, the Computer Vision and Inertial Position Reference Sensor System (CVIPRSS, pronounced \nickname), combines a laser rangefinder, infrared camera, and a pan--tilt unit with the robust TLD (Tracking--Learning--Detection) object tracker. In this work, a \nickname ~prototype is evaluated, showing promising results as viable PRS with research, commercial, and industrial applications

Smartphone-based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

Existing artificial intelligence solutions typically operate in powerful platforms with high computational resources availability. However, a growing number of emerging use cases such as those based on unmanned aerial systems (UAS) require new solutions with embedded artificial intelligence on a highly mobile platform. This paper proposes an innovative UAS that explores machine learning (ML) capabilities in a smartphone‐based mobile platform for object detection and recognition applications. A new system framework tailored to this challenging use case is designed with a customized workflow specified. Furthermore, the design of the embedded ML leverages TensorFlow, a cutting‐edge open‐source ML framework. The prototype of the system integrates all the architectural components in a fully functional system, and it is suitable for real‐world operational environments such as seek and rescue use cases. Experimental results validate the design and prototyping of the system and demonstrate an overall improved performance compared with the state of the art in terms of a wide range of metrics

Remote estimation of target height using unmanned air vehicles (UAVs)

Dissertation presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Business Intelligence and Knowledge ManagementEstimation of target height from videos is used for several applications, such as monitoring agricultural plants growth or, within surveillance scenarios, supporting the identification of persons of interest. Several studies have been conducted in this domain but, in almost all the cases, only fixed cameras were considered. Nowadays, lightweight UAVs are often employed for remote monitoring and surveillance activities due to their mobility capacity and freedom for camera orientation. This paper focuses on how the height could be swiftly performed with a gimballed camera installed into a UAV using a pinhole camera model after camera calibration and image distortion compensation. The model is tailored for UAV applications outdoor and generalized for any camera orientations defined by Euler angles. The procedure was tested with real data collected with a regular-market lightweight quad-copter. The data collected was also used to make an uncertainty analysis associated with the estimation. Finally, since the height of a person who is not standing perfectly vertical can be derived by relationships between body parts or human face features ratio, this paper proposes to retrieve the pixel spacing measured along the vertical target, called here Vertical Sample Distance (VSD), to quickly measure vertical sub-portions of the target

An Image-Based Real-Time Georeferencing Scheme for a UAV Based on a New Angular Parametrization

Simultaneous localization and mapping (SLAM) of a monocular projective camera installed on an unmanned aerial vehicle (UAV) is a challenging task in photogrammetry, computer vision, and robotics. This paper presents a novel real-time monocular SLAM solution for UAV applications. It is based on two steps: consecutive construction of the UAV path, and adjacent strip connection. Consecutive construction rapidly estimates the UAV path by sequentially connecting incoming images to a network of connected images. A multilevel pyramid matching is proposed for this step that contains a sub-window matching using high-resolution images. The sub-window matching increases the frequency of tie points by propagating locations of matched sub-windows that leads to a list of high-frequency tie points while keeping the execution time relatively low. A sparse bundle block adjustment (BBA) is employed to optimize the initial path by considering nuisance parameters. System calibration parameters with respect to global navigation satellite system (GNSS) and inertial navigation system (INS) are optionally considered in the BBA model for direct georeferencing. Ground control points and checkpoints are optionally included in the model for georeferencing and quality control. Adjacent strip connection is enabled by an overlap analysis to further improve connectivity of local networks. A novel angular parametrization based on spherical rotation coordinate system is presented to address the gimbal lock singularity of BBA. Our results suggest that the proposed scheme is a precise real-time monocular SLAM solution for a UAV.Peer reviewe