281 research outputs found
Appearance modeling for persistent object tracking in wide-area and full motion video
Object tracking is a core element of computer vision and autonomous systems. As such single and multiple object tracking has been widely investigated especially for full motion video sequences. The acquisition of wide-area motion imagery (WAMI) from moving airborne platforms is a much more recent sensor innovation that has an array of defense and civilian applications with numerous opportunities for providing a unique combination of dense spatial and temporal coverage unmatched by other sensor systems. Airborne WAMI presents a host of challenges for object tracking including large data volume, multi-camera arrays, image stabilization, low resolution targets, target appearance variability and high background clutter especially in urban environments. Time varying low frame rate large imagery poses a range of difficulties in terms of reliable long term multi-target tracking. The focus of this thesis is on the Likelihood of Features Tracking (LOFT) testbed system that is an appearance based (single instance) object tracker designed specifcally for WAMI and follows the track before detect paradigm. The motivation for tracking using dynamics before detecting was so that large scale data can be handled in an environment where computational cost can be kept at a bare minimum. Searching for an object everywhere on a large frame is not practical as there are many similar objects, clutter, high rise structures in case of urban scenes and comes with the additional burden of greatly increased computational cost. LOFT bypasses this difficulty by using filtering and dynamics to constrain the search area to a more realistic region within the large frame and uses multiple features to discern objects of interest. The objects of interest are expected as input in the form of bounding boxes to the algorithm. The main goal of this work is to present an appearance update modeling strategy that fits LOFT's track before detect paradigm and to showcase the accuracy of the overall system as compared with other state of the art tracking algorithms and also with and without the presence of this strategy. The update strategy using various information cues from the Radon Transform was designed with certain performance parameters in mind such as minimal increase in computational cost and a considerable increase in precision and recall rates of the overall system. This has been demonstrated with supporting performance numbers using standard evaluation techniques as in literature. The extensions of LOFT WAMI tracker to include a more detailed appearance model with an update strategy that is well suited for persistent target tracking is novel in the opinion of the author. Key engineering contributions have been made with the help of this work wherein the core LOFT has been evaluated as part several government research and development programs including the Air Force Research Lab's Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) Enterprise to the Edge (CETE), Army Research Lab's Advanced Video Activity Analytics (AVAA) and a proposed fine grained distributed computing architecture on the cloud for processing at the edge. A simplified version of LOFT was developed for tracking objects in standard videos and entered in the Visual Object Tracking (VOT) Challenge competition that is held in conjunction with the leading computer vision conferences. LOFT incorporating the proposed appearance adaptation module produces significantly better tracking results in aerial WAMI of urban scenes
Machine Vision: How Algorithms are Changing the Way We See the World
Humans have used technology to expand our limited vision for millennia, from the invention of the stone mirror 8,000 years ago to the latest developments in facial recognition and augmented reality. We imagine that technologies will allow us to see more, to see differently and even to see everything. But each of these new ways of seeing carries its own blind spots. In this illuminating book, Jill Walker Rettberg examines the long history of machine vision. Providing an overview of the historical and contemporary uses of machine vision, she unpacks how technologies such as smart surveillance cameras and TikTok filters are changing the way we see the world and one another. By analysing fictional and real-world examples, including art, video games and science fiction, the book shows how machine vision can have very different cultural impacts, fostering both sympathy and community as well as anxiety and fear. Combining ethnographic and critical media studies approaches alongside personal reflections, Machine Vision is an engaging and eye-opening read. It is suitable for students and scholars of digital media studies, science and technology studies, visual studies, digital art and science fiction, as well as for general readers interested in the impact of new technologies on society.publishedVersio
Counter Unmanned Aircraft Systems Technologies and Operations
As the quarter-century mark in the 21st Century nears, new aviation-related equipment has come to the forefront, both to help us and to haunt us. (Coutu, 2020) This is particularly the case with unmanned aerial vehicles (UAVs). These vehicles have grown in popularity and accessible to everyone. Of different shapes and sizes, they are widely available for purchase at relatively low prices. They have moved from the backyard recreation status to important tools for the military, intelligence agencies, and corporate organizations. New practical applications such as military equipment and weaponry are announced on a regular basis – globally. (Coutu, 2020) Every country seems to be announcing steps forward in this bludgeoning field.
In our successful 2nd edition of Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets (Nichols, et al., 2019), the authors addressed three factors influencing UAS phenomena. First, unmanned aircraft technology has seen an economic explosion in production, sales, testing, specialized designs, and friendly / hostile usages of deployed UAS / UAVs / Drones. There is a huge global growing market and entrepreneurs know it. Second, hostile use of UAS is on the forefront of DoD defense and offensive planners. They are especially concerned with SWARM behavior. Movies like “Angel has Fallen,” where drones in a SWARM use facial recognition technology to kill USSS agents protecting POTUS, have built the lore of UAS and brought the problem forefront to DHS. Third, UAS technology was exploding. UAS and Counter- UAS developments in navigation, weapons, surveillance, data transfer, fuel cells, stealth, weight distribution, tactics, GPS / GNSS elements, SCADA protections, privacy invasions, terrorist uses, specialized software, and security protocols has exploded. (Nichols, et al., 2019) Our team has followed / tracked joint ventures between military and corporate entities and specialized labs to build UAS countermeasures.
As authors, we felt compelled to address at least the edge of some of the new C-UAS developments. It was clear that we would be lucky if we could cover a few of – the more interesting and priority technology updates – all in the UNCLASSIFIED and OPEN sphere.
Counter Unmanned Aircraft Systems: Technologies and Operations is the companion textbook to our 2nd edition. The civilian market is interesting and entrepreneurial, but the military and intelligence markets are of concern because the US does NOT lead the pack in C-UAS technologies. China does. China continues to execute its UAS proliferation along the New Silk Road Sea / Land routes (NSRL). It has maintained a 7% growth in military spending each year to support its buildup. (Nichols, et al., 2019) [Chapter 21]. They continue to innovate and have recently improved a solution for UAS flight endurance issues with the development of advanced hydrogen fuel cell. (Nichols, et al., 2019) Reed and Trubetskoy presented a terrifying map of countries in the Middle East with armed drones and their manufacturing origin. Guess who? China. (A.B. Tabriski & Justin, 2018, December)
Our C-UAS textbook has as its primary mission to educate and train resources who will enter the UAS / C-UAS field and trust it will act as a call to arms for military and DHS planners.https://newprairiepress.org/ebooks/1031/thumbnail.jp
Articulated human tracking and behavioural analysis in video sequences
Recently, there has been a dramatic growth of interest in the observation and tracking
of human subjects through video sequences. Arguably, the principal impetus has come
from the perceived demand for technological surveillance, however applications in entertainment,
intelligent domiciles and medicine are also increasing. This thesis examines
human articulated tracking and the classi cation of human movement, rst separately
and then as a sequential process.
First, this thesis considers the development and training of a 3D model of human body
structure and dynamics. To process video sequences, an observation model is also designed
with a multi-component likelihood based on edge, silhouette and colour. This is de ned on
the articulated limbs, and visible from a single or multiple cameras, each of which may be
calibrated from that sequence. Second, for behavioural analysis, we develop a methodology
in which actions and activities are described by semantic labels generated from a Movement
Cluster Model (MCM). Third, a Hierarchical Partitioned Particle Filter (HPPF) was
developed for human tracking that allows multi-level parameter search consistent with the
body structure. This tracker relies on the articulated motion prediction provided by the
MCM at pose or limb level. Fourth, tracking and movement analysis are integrated to
generate a probabilistic activity description with action labels.
The implemented algorithms for tracking and behavioural analysis are tested extensively
and independently against ground truth on human tracking and surveillance
datasets. Dynamic models are shown to predict and generate synthetic motion, while
MCM recovers both periodic and non-periodic activities, de ned either on the whole body
or at the limb level. Tracking results are comparable with the state of the art, however
the integrated behaviour analysis adds to the value of the approach.Overseas Research Students Awards Scheme (ORSAS
Journey of Artificial Intelligence Frontier: A Comprehensive Overview
The field of Artificial Intelligence AI is a transformational force with limitless promise in the age of fast technological growth This paper sets out on a thorough tour through the frontiers of AI providing a detailed understanding of its complex environment Starting with a historical context followed by the development of AI seeing its beginnings and growth On this journey fundamental ideas are explored looking at things like Machine Learning Neural Networks and Natural Language Processing Taking center stage are ethical issues and societal repercussions emphasising the significance of responsible AI application This voyage comes to a close by looking ahead to AI s potential for human-AI collaboration ground-breaking discoveries and the difficult obstacles that lie ahead This provides with a well-informed view on AI s past present and the unexplored regions it promises to explore by thoroughly navigating this terrai
Tracking Interacting Objects in Image Sequences
Object tracking in image sequences is a key challenge in computer vision. Its goal is to follow objects that move or evolve over time while preserving the identity of each object. However, most existing approaches focus on one class of objects and model only very simple interactions, such as the fact that different objects do not occupy the same spatial location at a given time instance. They ignore that objects may interact in more complex ways. For example, in a parking lot, a person may get in a car and become invisible in the scene. In this thesis, we focus on tracking interacting objects in image sequences. We show that by exploiting the relationship between different objects, we can achieve more reliable tracking results. We explore a wide range of applications, such as tracking players and the ball in team sports, tracking cars and people in a parking lot and tracking dividing cells in biomedical imagery. We start by tracking the ball in team sports, which is a very challenging task because the ball is often occluded by the players. We propose a sequential approach that tracks the players first, and then tracks the ball by deciding which player, if any, is in possession of the ball at any given time. This is very different from standard approaches that first attempt to track the ball and only then to assign possession. We show that our method substantially increases performance when applied to long basketball and soccer sequences. We then focus on simultaneously tracking interacting objects. We achieve this by formulating the tracking problem as a network-flow Mixed Integer Program, and expressing the fact that one object can appear or disappear at locations of another in terms of linear flow constraints. We demonstrate our method on scenes involving cars and passengers, bags being carried and dropped by people, and balls being passed from one player to the next in team sports. In particular, we show that by estimating jointly and globally the trajectories of different types of objects, the presence of the ones which were not initially detected based solely on image evidence can be inferred from the detections of the others. We finally extend our approach to dividing cells in biomedical imagery. In this case, cells interact by overlapping with each other and giving birth to daughter cells. We propose a novel approach to automatically detecting and tracking cell populations in time-lapse images. Unlike earlier approaches that rely on linking a predetermined and potentially incomplete set of detections, we generate an overcomplete set of competing detection hypotheses. We then perform detection and tracking simultaneously by solving an integer program to find the optimal and consistent subset. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging image sequences consisting of clumped cells and show that it outperforms the state-of-the-art techniques
Multi-Robot Systems: Challenges, Trends and Applications
This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics
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