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

Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery

A robust and fast automatic moving object detection and tracking system is essential to characterize target object and extract spatial and temporal information for different functionalities including video surveillance systems, urban traffic monitoring and navigation, robotic. In this dissertation, I present a collaborative Spatial Pyramid Context-aware moving object detection and Tracking system. The proposed visual tracker is composed of one master tracker that usually relies on visual object features and two auxiliary trackers based on object temporal motion information that will be called dynamically to assist master tracker. SPCT utilizes image spatial context at different level to make the video tracking system resistant to occlusion, background noise and improve target localization accuracy and robustness. We chose a pre-selected seven-channel complementary features including RGB color, intensity and spatial pyramid of HoG to encode object color, shape and spatial layout information. We exploit integral histogram as building block to meet the demands of real-time performance. A novel fast algorithm is presented to accurately evaluate spatially weighted local histograms in constant time complexity using an extension of the integral histogram method. Different techniques are explored to efficiently compute integral histogram on GPU architecture and applied for fast spatio-temporal median computations and 3D face reconstruction texturing. We proposed a multi-component framework based on semantic fusion of motion information with projected building footprint map to significantly reduce the false alarm rate in urban scenes with many tall structures. The experiments on extensive VOTC2016 benchmark dataset and aerial video confirm that combining complementary tracking cues in an intelligent fusion framework enables persistent tracking for Full Motion Video and Wide Aerial Motion Imagery.Comment: PhD Dissertation (162 pages

Status of NASA/Army rotorcraft research and development piloted flight simulation

The status of the major NASA/Army capabilities in piloted rotorcraft flight simulation is reviewed. The requirements for research and development piloted simulation are addressed as well as the capabilities and technologies that are currently available or are being developed by NASA and the Army at Ames. The application of revolutionary advances (in visual scene, electronic cockpits, motion, and modelling of interactive mission environments and/or vehicle systems) to the NASA/Army facilities are also addressed. Particular attention is devoted to the major advances made in integrating these individual capabilities into fully integrated simulation environment that were or are being applied to new rotorcraft mission requirements. The specific simulators discussed are the Vertical Motion Simulator and the Crew Station Research and Development Facility

Overview of contextual tracking approaches in information fusion

Proceedings of: Geospatial InfoFusion III. 2-3 May 2013 Baltimore, Maryland, United States.Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.Publicad

Conceptual design study for an advanced cab and visual system, volume 2

The performance, design, construction and testing requirements are defined for developing an advanced cab and visual system. The rotorcraft system integration simulator is composed of the advanced cab and visual system and the rotorcraft system motion generator, and is part of an existing simulation facility. User's applications for the simulator include rotorcraft design development, product improvement, threat assessment, and accident investigation

Dense Point-Cloud Representation of a Scene using Monocular Vision

We present a three-dimensional (3-D) reconstruction system designed to support various autonomous navigation applications. The system presented focuses on the 3-D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the depths of a scene. In this way, the system can be used to construct a point-cloud model of its unknown surroundings. We present the step-by-step methodology and analysis used in developing the 3-D reconstruction technique. We present a reconstruction framework that generates a primitive point cloud, which is computed based on feature matching and depth triangulation analysis. To populate the reconstruction, we utilized optical flow features to create an extremely dense representation model. With the third algorithmic modification, we introduce the addition of the preprocessing step of nonlinear single-image super resolution. With this addition, the depth accuracy of the point cloud, which relies on precise disparity measurement, has significantly increased. Our final contribution is an additional postprocessing step designed to filter noise points and mismatched features unveiling the complete dense point-cloud representation (DPR) technique. We measure the success of DPR by evaluating the visual appeal, density, accuracy, and computational expense and compare with two state-of-the-art techniques

Characteristics of flight simulator visual systems

The physical parameters of the flight simulator visual system that characterize the system and determine its fidelity are identified and defined. The characteristics of visual simulation systems are discussed in terms of the basic categories of spatial, energy, and temporal properties corresponding to the three fundamental quantities of length, mass, and time. Each of these parameters are further addressed in relation to its effect, its appropriate units or descriptors, methods of measurement, and its use or importance to image quality