Crowd detection and counting using a static and dynamic platform: state of the art

Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms

Applications of satellite and marine geodesy to operations in the ocean environment

The requirements for marine and satellite geodesy technology are assessed with emphasis on the development of marine geodesy. Various programs and missions for identification of the satellite geodesy technology applicable to marine geodesy are analyzed along with national and international marine programs to identify the roles of satellite/marine geodesy techniques for meeting the objectives of the programs and other objectives of national interest effectively. The case for marine geodesy is developed based on the extraction of requirements documented by authoritative technical industrial people, professional geodesists, government agency personnel, and applicable technology reports

Gaussian mixture model classifiers for detection and tracking in UAV video streams.

Masters Degree. University of KwaZulu-Natal, Durban.Manual visual surveillance systems are subject to a high degree of human-error and operator fatigue. The automation of such systems often employs detectors, trackers and classifiers as fundamental building blocks. Detection, tracking and classification are especially useful and challenging in Unmanned Aerial Vehicle (UAV) based surveillance systems. Previous solutions have addressed challenges via complex classification methods. This dissertation proposes less complex Gaussian Mixture Model (GMM) based classifiers that can simplify the process; where data is represented as a reduced set of model parameters, and classification is performed in the low dimensionality parameter-space. The specification and adoption of GMM based classifiers on the UAV visual tracking feature space formed the principal contribution of the work. This methodology can be generalised to other feature spaces. This dissertation presents two main contributions in the form of submissions to ISI accredited journals. In the first paper, objectives are demonstrated with a vehicle detector incorporating a two stage GMM classifier, applied to a single feature space, namely Histogram of Oriented Gradients (HoG). While the second paper demonstrates objectives with a vehicle tracker using colour histograms (in RGB and HSV), with Gaussian Mixture Model (GMM) classifiers and a Kalman filter. The proposed works are comparable to related works with testing performed on benchmark datasets. In the tracking domain for such platforms, tracking alone is insufficient. Adaptive detection and classification can assist in search space reduction, building of knowledge priors and improved target representations. Results show that the proposed approach improves performance and robustness. Findings also indicate potential further enhancements such as a multi-mode tracker with global and local tracking based on a combination of both papers

Novel Methods for Forensic Multimedia Data Analysis: Part I

The increased usage of digital media in daily life has resulted in the demand for novel multimedia data analysis techniques that can help to use these data for forensic purposes. Processing of such data for police investigation and as evidence in a court of law, such that data interpretation is reliable, trustworthy, and efficient in terms of human time and other resources required, will help greatly to speed up investigation and make investigation more effective. If such data are to be used as evidence in a court of law, techniques that can confirm origin and integrity are necessary. In this chapter, we are proposing a new concept for new multimedia processing techniques for varied multimedia sources. We describe the background and motivation for our work. The overall system architecture is explained. We present the data to be used. After a review of the state of the art of related work of the multimedia data we consider in this work, we describe the method and techniques we are developing that go beyond the state of the art. The work will be continued in a Chapter Part II of this topic

Privacy in the Age of Contact Tracing: An Analysis of Contact Tracing Apps in Different Statutory and Disease Frameworks

The Covid-19 pandemic is a historic pandemic that has affected the lives of virtually everyone on the globe. One approach to slowing the spread of the disease is to use contact tracing, facilitated by our internet-connected smartphones. Different nations and states have partnered to develop a variety of contact tracing apps that use different technologies and architectures. This paper investigates how five contact tracing apps—Germany’s Corona-Warn-App, Israel’s HaMagen, North Dakota’s Care19 Diary and Alert apps, and India’s Aarogya Setu—fare in privacy-oriented statutory frameworks to understand the design choices and public health implications shaped by these statutes. The three statutes—the Health Insurance Portability and Accountability Act, the California Consumer Privacy Act, and the European Union’s General Data Protection Regulation—provide different incentives to app developers across eight categories of design choices: notice and consent, consent requirements for medical data disclosed to third parties, location identifying technologies, data profiles and data collection, minimizing data categories collected, data sale and sharing with nonresearch third parties, third party and researcher access to data, and affirmative user rights. Each framework balances incentives to app developers with the need for governments to cater to pressing emergencies like public health needs. Some of the incentives in each framework end up favoring less privacy-protective design choices, whereas other provisions make it harder for public health authorities to flexibly respond to crises. Finally, this paper investigates how these frameworks would fare with different disease variables, by applying the analysis above to three different diseases that could require contact tracing: SARS, Ebola, and HIV. Our conclusion is that the disease variables themselves will affect whether the balance tilts towards public health or privacy, and that the statutes give varying levels of flexibility to cater to more pressing emergencies

Image-based traffic monitoring system.

Lau Wai Hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 63-65).Abstracts in English and Chinese.abstract --- p.I摘要 --- p.IIacknowledgement --- p.IIItable of contents --- p.IVlist of figures --- p.VIChapter CHAPTER 1 --- introduction --- p.1Chapter CHAPTER 2 --- literature review --- p.4Chapter 2.1 --- Traffic data collection methods --- p.4Chapter 2.2 --- Vision-based traffic monitoring techniques --- p.6Chapter 2.2.1 --- Vehicle tracking approaches --- p.7Chapter 2.2.2 --- Image processing techniques --- p.10Chapter CHAPTER 3 --- methodology --- p.15Chapter 3.1 --- Solution Concept --- p.16Chapter 3.2 --- System Framework --- p.18Chapter 3.2.1 --- Edge Detection Module --- p.20Chapter 3.2.2 --- Background Update Module --- p.22Chapter 3.2.3 --- Feature Extraction Modules --- p.25Chapter CHAPTER 4 --- experiments and evaluation --- p.41Chapter 4.1 --- Setup and Data Collection --- p.41Chapter 4.2 --- Evaluation Criteria --- p.42Chapter 4.3 --- Experimental Results --- p.44Chapter 4.3.1 --- Comparing overall accuracies --- p.44Chapter 4.3.2 --- Accuracies for different traffic conditions --- p.46Chapter 4.3.3 --- Comparing balanced sampling and random sampling --- p.48Chapter 4.3.4 --- Comparing day and night conditions --- p.50Chapter 4.3.5 --- Testing on time-series of images --- p.52Chapter CHAPTER 5 --- analysis --- p.54Chapter 5.1 --- Strengths and Weaknesses --- p.54Chapter 5.1.1 --- Sobel Edge Histogram --- p.54Chapter 5.1.2 --- Horizontal Line Detection --- p.55Chapter 5.1.3 --- Block Detection --- p.56Chapter 5.1.4 --- Combined Learning --- p.57Chapter 5.1.5 --- Overall Framework --- p.58Chapter 5.2 --- Future Research --- p.59Chapter 5.2.1 --- Static image based monitoring combined with other traffic monitoring approaches --- p.59Chapter 5.2.2 --- Horizontal Line Detection as tracked features of vehicles --- p.60Chapter 5.2.3 --- Application in aerial image-based system --- p.60Chapter CHAPTER 6 --- conclusion --- p.62bibliography --- p.63appendix a sobel edge detection --- p.66appendix b neural network setup --- p.67appendix c numerical results --- p.6

Robust Methods for Accurate and Efficient Reconstruction from Motion Imagery

Creating virtual representations of real-world scenes has been a long-standing goal in photogrammetry and computer vision, and has high practical relevance in industries involved in creating intelligent urban solutions. This includes a wide range of applications such as urban and community planning, reconnaissance missions by the military and government, autonomous robotics, virtual reality, cultural heritage preservation, and many others. Over the last decades, image-based modeling emerged as one of the most popular solutions. The objective is to extract metric information directly from images. Many procedural techniques achieve good results in terms of robustness, accuracy, completeness, and efficiency. More recently, deep-learning-based techniques were proposed to tackle this problem by training on vast amounts of data to learn to associate features between images through deep convolutional neural networks and were shown to outperform traditional procedural techniques. However, many of the key challenges such as large displacement and scalability still remain, especially when dealing with large-scale aerial imagery. This thesis investigates image-based modeling and proposes robust and scalable methods for large-scale aerial imagery. First, we present a method for reconstructing large-scale areas from aerial imagery that formulates the solution as a single-step process, reducing the processing time considerably. Next, we address feature matching and propose a variational optical flow technique (HybridFlow) for dense feature matching that leverages the robustness of graph matching to large displacements. The proposed solution efficiently handles arbitrary-sized aerial images. Finally, for general-purpose image-based modeling, we propose a deep-learning-based approach, an end-to-end multi-view structure from motion employing hypercorrelation volumes for learning dense feature matches. We demonstrate the application of the proposed techniques on several applications and report on task-related measures

Leveraging Metadata for Computer Vision on Unmanned Aerial Vehicles

The integration of computer vision technology into Unmanned Aerial Vehicles (UAVs) has become increasingly crucial in various aerial vision-based applications. Despite the great significant success of generic computer vision methods, a considerable performance drop is observed when applied to the UAV domain. This is due to large variations in imaging conditions, such as varying altitudes, dynamically changing viewing angles, and varying capture times resulting in vast changes in lighting conditions. Furthermore, the need for real-time algorithms and the hardware constraints pose specific problems that require special attention in the development of computer vision algorithms for UAVs. In this dissertation, we demonstrate that domain knowledge in the form of meta data is a valuable source of information and thus propose domain-aware computer vision methods by using freely accessible sensor data. The pipeline for computer vision systems on UAVs is discussed, from data mission planning, data acquisition, labeling and curation, to the construction of publicly available benchmarks and leaderboards and the establishment of a wide range of baseline algorithms. Throughout, the focus is on a holistic view of the problems and opportunities in UAV-based computer vision, and the aim is to bridge the gap between purely software-based computer vision algorithms and environmentally aware robotic platforms. The results demonstrate that incorporating meta data obtained from onboard sensors, such as GPS, barometers, and inertial measurement units, can significantly improve the robustness and interpretability of computer vision models in the UAV domain. This leads to more trustworthy models that can overcome challenges such as domain bias, altitude variance, synthetic data inefficiency, and enhance perception through environmental awareness in temporal scenarios, such as video object detection, tracking and video anomaly detection. The proposed methods and benchmarks provide a foundation for future research in this area, and the results suggest promising directions for developing environmentally aware robotic platforms. Overall, this work highlights the potential of combining computer vision and robotics to tackle real-world challenges and opens up new avenues for interdisciplinary research

Intelligent Control Agent for Autonomous UAS

A self reconfiguring autopilot system is presented, which is based on a rational agent framework that integrates decision making with abstractions of sensing and actions for next generation unmanned aerial vehicles. The objective of the new intelligent control system is to provide advanced capabilities of self-tuning control for a new UAS airframe or adaptation for an old UAS in the presence of failures in adverse flight conditions. High-level system performance is achieved through on-board dynamical monitoring and estimation associated with controller switching and tuning by the agent. The agent can handle an untuned autopilot or retune the autopilot when dynamical changes occur due to aerodynamic and on-board system changes. The system integrates dynamical modelling, hybrid adaptive control, model validation, flight condition diagnosis, control performance evaluation through software agent development. An important feature of the agent is its abstractions from real-time measurements and also its abstractions from model based on-board simulation. The agent, while tuning and supervising the autopilot, also performs real-time evaluations on the effects of its actions