Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and Pattern Recognition Workshops, 201

SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm

The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications

Multilevel analysis of nuclear dynamics in lamin perturbed fibroblasts

The nuclear lamina provides structural support to the nucleus and has a central role in defining nuclear organization. Defects in its filamentous constituents, the lamins, lead to a class of diseases collectively referred to as laminopathies. On the cellular level, lamin mutations affect the physical integrity of nuclei and nucleo-cytoskeletal interactions, resulting in increased susceptibility to mechanical stress and altered gene expression [1]. Most studies regarding the mechanical properties of the nucleus in laminopathic conditions are based on the induction of extracellular stress, such as strain or compression, and focus on nuclear integrity and/or nucleo-cytoskeletal interaction [2]. Far less is known about the role of nuclear organization and mobility under basal steady-state conditions. In this study, we quantitatively compared nuclear organization, nuclear deformation and chromatin mobility of fibroblasts from a Hutchinson-Gilford progeria patient with cells from a lamin A/C-deficient patient and wild-type dermal fibroblasts. To this end, we created a toolbox in imageJ for automatically analyzing both nuclear as well as subnuclear dynamics in living cells. Simultaneously, we developed a workflow for comparing cellular morphology and subcellular protein distribution in a high content fashion. We found that the absence of functional lamin A/C leads to increased nuclear plasticity on the hour and minute time scale but also to increased intranuclear mobility down to the seconds time scale. In contrast, progeria cells showed overall reduced nuclear dynamics. In addition, high content analysis revealed marked morphological and topological differences between different culture passages within a cell type and between different pathological variants of culture-age matched laminopathic cell types

Real-time Aerial Vehicle Detection and Tracking using a Multi-modal Optical Sensor

Vehicle tracking from an aerial platform poses a number of unique challenges including the small number of pixels representing a vehicle, large camera motion, and parallax error. For these reasons, it is accepted to be a more challenging task than traditional object tracking and it is generally tackled through a number of different sensor modalities. Recently, the Wide Area Motion Imagery sensor platform has received reasonable attention as it can provide higher resolution single band imagery in addition to its large area coverage. However, still, richer sensory information is required to persistently track vehicles or more research on the application of WAMI for tracking is required. With the advancements in sensor technology, hyperspectral data acquisition at video frame rates become possible as it can be cruical in identifying objects even in low resolution scenes. For this reason, in this thesis, a multi-modal optical sensor concept is considered to improve tracking in adverse scenes. The Rochester Institute of Technology Multi-object Spectrometer is capable of collecting limited hyperspectral data at desired locations in addition to full-frame single band imagery. By acquiring hyperspectral data quickly, tracking can be achieved at reasonableframe rates which turns out to be crucial in tracking. On the other hand, the relatively high cost of hyperspectral data acquisition and transmission need to be taken into account to design a realistic tracking. By inserting extended data of the pixels of interest we can address or avoid the unique challenges posed by aerial tracking. In this direction, we integrate limited hyperspectral data to improve measurement-to-track association. Also, a hyperspectral data based target detection method is presented to avoid the parallax effect and reduce the clutter density. Finally, the proposed system is evaluated on realistic, synthetic scenarios generated by the Digital Image and Remote Sensing software