J-MOD2^{2}: Joint Monocular Obstacle Detection and Depth Estimation

In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches either rely on Visual SLAM systems or on depth estimation models to build 3D maps and detect obstacles. However, for the task of avoiding obstacles this level of complexity is not required. Recent works have proposed multi task architectures to both perform scene understanding and depth estimation. We follow their track and propose a specific architecture to jointly estimate depth and obstacles, without the need to compute a global map, but maintaining compatibility with a global SLAM system if needed. The network architecture is devised to exploit the joint information of the obstacle detection task, that produces more reliable bounding boxes, with the depth estimation one, increasing the robustness of both to scenario changes. We call this architecture J-MOD$^{2}$. We test the effectiveness of our approach with experiments on sequences with different appearance and focal lengths and compare it to SotA multi task methods that jointly perform semantic segmentation and depth estimation. In addition, we show the integration in a full system using a set of simulated navigation experiments where a MAV explores an unknown scenario and plans safe trajectories by using our detection model

Probabilistic Models for Joint Segmentation, Detection and Tracking

Migrace buněk a buněčných částic hraje důležitou roli ve fungování živých organismů. Systematický výzkum buněčné migrace byl umožněn v posledních dvaceti letech rychlým rozvojem neinvazivních zobrazovacích technik a digitálních snímačů. Moderní zobrazovací systémy dovolují studovat chování buněčných populací složených z mnoha ticíců buněk. Manuální analýza takového množství dat by byla velice zdlouhavá, protože některé experimenty vyžadují analyzovat tvar, rychlost a další charakteristiky jednotlivých buněk. Z tohoto důvodu je ve vědecké komunitě velká poptávka po automatických metodách.Migration of cells and subcellular particles plays a crucial role in many processes in living organisms. Despite its importance a systematic research of cell motility has only been possible in last two decades due to rapid development of non-invasive imaging techniques and digital cameras. Modern imaging systems allow to study large populations with thousands of cells. Manual analysis of the acquired data is infeasible, because in order to gain insight into underlying biochemical processes it is sometimes necessary to determine shape, velocity and other characteristics of individual cells. Thus there is a high demand for automatic methods

DeepPOSE: Detecting GPS Spoofing Attack Via Deep Recurrent Neural Network

The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this end, we propose DeepPOSE, a deep learning model, to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms. Our design uses a convolutional and recurrent neural network to reduce the noise, to recover a vehicle\u27s real-time trajectory from multiple sensor inputs. We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map, to smartly eliminate the accumulation of errors on the trajectory estimation. The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack. Compared with the existing method, the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks

Optimal path planning for detection and classification of underwater targets using sonar

2021 Spring.Includes bibliographical references.The work presented in this dissertation focuses on choosing an optimal path for performing sequential detection and classification state estimation to identify potential underwater targets using sonar imagery. The detection state estimation falls under the occupancy grid framework, modeling the relationship between occupancy state of grid cells and sensor measurements, and allows for the consideration of statistical dependence between the occupancy state of each grid cell in the map. This is in direct contrast to the classical formulations of occupancy grid frameworks, in which the occupancy state of each grid cell is considered statistically independent. The new method provides more accurate estimates, and occupancy grids estimated with this method typically converge with fewer measurements. The classification state estimation utilises a Dirichlet-Categorical model and a one-step classifier to perform efficient updating of the classification state estimate for each grid cell. To show the performance capabilities of the developed sequential state estimation methods, they are applied to sonar systems in littoral areas in which targets lay on the seafloor, could be proud, partially or fully buried. Additionally, a new approach to the active perception problem, which seeks to select a series of sensing actions that provide the maximal amount of information to the system, is developed. This new approach leverages the aforementioned sequential state estimation techniques to develop a set of information-theoretic cost functions that can be used for optimal sensing action selection. A path planning cost function is developed, defined as the mutual information between the aforementioned state variables before and after a measurement. The cost function is expressed in closed form by considering the prior and posterior distributions of the state variables. Choice of the optimal sensing actions is performed by modeling the path planning as a Markov decision problem, and solving it with the rollout algorithm. This work, supported by the Office of Naval Research (ONR), is intended to develop a suite of interactive sensing algorithms to autonomously command an autonomous underwater vehicle (AUV) for the task of detection and classification of underwater mines, while choosing an optimal navigation route that increases the quality of the detection and classification state estimates

A New Strategy for Deep Wide-Field High Resolution Optical Imaging

We propose a new strategy for obtaining enhanced resolution (FWHM = 0.12 arcsec) deep optical images over a wide field of view. As is well known, this type of image quality can be obtained in principle simply by fast guiding on a small (D = 1.5m) telescope at a good site, but only for target objects which lie within a limited angular distance of a suitably bright guide star. For high altitude turbulence this 'isokinetic angle' is approximately 1 arcminute. With a 1 degree field say one would need to track and correct the motions of thousands of isokinetic patches, yet there are typically too few sufficiently bright guide stars to provide the necessary guiding information. Our proposed solution to these problems has two novel features. The first is to use orthogonal transfer charge-coupled device (OTCCD) technology to effectively implement a wide field 'rubber focal plane' detector composed of an array of cells which can be guided independently. The second is to combine measured motions of a set of guide stars made with an array of telescopes to provide the extra information needed to fully determine the deflection field. We discuss the performance, feasibility and design constraints on a system which would provide the collecting area equivalent to a single 9m telescope, a 1 degree square field and 0.12 arcsec FWHM image quality.Comment: 46 pages, 22 figures, submitted to PASP, a version with higher resolution images and other supplementary material can be found at http://www.ifa.hawaii.edu/~kaiser/wfhr

Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis

Deep learning is transforming the ability of life scientists to extract information from images. While these techniques have superior accuracy in comparison to conventional approaches and enable previously impossible analyses, their unique hardware and software requirements have prevented widespread adoption by life scientists. To meet this need, we have developed DeepCell 2.0, an open source library for training and delivering deep learning models with cloud computing. This library enables users to configure and manage a cloud deployment of DeepCell 2.0 on all commonly used operating systems. Using single-cell segmentation as a use case, we show that users with suitable training data can train models and analyze data with those models through a web interface. We demonstrate that by matching analysis tasks with their hardware requirements, we can efficiently use computational resources in the cloud and scale those resources to meet demand, significantly reducing the time necessary for large-scale image analysis. By reducing the barriers to entry, this work will empower life scientists to apply deep learning methods to their data. A persistent deployment is available at http://www.deepcell.org