Software Porting of a 3D Reconstruction Algorithm to Razorcam Embedded System on Chip

A method is presented to calculate depth information for a UAV navigation system from Keypoints in two consecutive image frames using a monocular camera sensor as input and the OpenCV library. This method was first implemented in software and run on a general-purpose Intel CPU, then ported to the RazorCam Embedded Smart-Camera System and run on an ARM CPU onboard the Xilinx Zynq-7000. The results of performance and accuracy testing of the software implementation are then shown and analyzed, demonstrating a successful port of the software to the RazorCam embedded system on chip that could potentially be used onboard a UAV with tight constraints of size, weight, and power. The potential impacts will be seen through the continuation of this research in the Smart ES lab at University of Arkansas

Unsupervised maritime target detection

The unsupervised detection of maritime targets in grey scale video is a difficult problem in maritime video surveillance. Most approaches assume that the camera is static and employ pixel-wise background modelling techniques for foreground detection; other methods rely on colour or thermal information to detect targets. These methods fail in real-world situations when the static camera assumption is violated, and colour or thermal data is unavailable. In defence and security applications, prior information and training samples of targets may be unavailable for training a classifier; the learning of a one class classifier for the background may be impossible as well. Thus, an unsupervised online approach that attempts to learn from the scene data is highly desirable. In this thesis, the characteristics of the maritime scene and the ocean texture are exploited for foreground detection. Two fast and effective methods are investigated for target detection. Firstly, online regionbased background texture models are explored for describing the appearance of the ocean. This approach avoids the need for frame registration because the model is built spatially rather than temporally. The texture appearance of the ocean is described using Local Binary Pattern (LBP) descriptors. Two models are proposed: one model is a Gaussian Mixture (GMM) and the other, referred to as a Sparse Texture Model (STM), is a set of histogram texture distributions. The foreground detections are optimized using a Graph Cut (GC) that enforces spatial coherence. Secondly, feature tracking is investigated as a means of detecting stable features in an image frame that typically correspond to maritime targets; unstable features are background regions. This approach is a Track-Before-Detect (TBD) concept and it is implemented using a hierarchical scheme for motion estimation, and matching of Scale- Invariant Feature Transform (SIFT) appearance features. The experimental results show that these approaches are feasible for foreground detection in maritime video when the camera is either static or moving. Receiver Operating Characteristic (ROC) curves were generated for five test sequences and the Area Under the ROC Curve (AUC) was analyzed for the performance of the proposed methods. The texture models, without GC optimization, achieved an AUC of 0.85 or greater on four out of the five test videos. At 50% True Positive Rate (TPR), these four test scenarios had a False Positive Rate (FPR) of less than 2%. With the GC optimization, an AUC of greater than 0.8 was achieved for all the test cases and the FPR was reduced in all cases when compared to the results without the GC. In comparison to the state of the art in background modelling for maritime scenes, our texture model methods achieved the best performance or comparable performance. The two texture models executed at a reasonable processing frame rate. The experimental results for TBD show that one may detect target features using a simple track score based on the track length. At 50% TPR a FPR of less than 4% is achieved for four out of the five test scenarios. These results are very promising for maritime target detection

Automated hand hygiene monitoring system using imagery and bluetooth low energy sensors

This thesis designs and implements a hand hygiene monitoring system using Bluetooth low energy and imagery sensors. As the cost of treating healthcare-associated infections increases, the need for monitoring and improving hand hygiene compliance percentages for healthcare providers increases. Several techniques for hand hygiene compliance monitoring exist, but it was found that electronic automated systems are the most reliable solution because they provide more accurate continuous compliance measurements for lower cost. Other similar systems based on a variety of technologies exist, however, they are either uniquely evidence based, so that they capture hygiene moments and apply a statistical model for hygiene opportunities, and they, therefore, do not provide real-time information; or they require human interference to determine compliance rendering them not fully automated. In this thesis, available monitoring techniques, focusing on automated electronic systems, are first introduced. Then, a novel automated hand hygiene monitoring system, capable of capturing hygiene moments with more than 90% precision, is proposed. The proposed system was first tested in a lab environment with private rooms setup, the system was also tested in semi-private rooms setup and then implemented in the Hematology and Oncology Department at the Health Sciences Center of Eastern Health for a pilot study. The study showed a high correlation between the compliance rates calculated by the proposed system compared to the compliance rates found by direct observers

ASSESSMENT OF ELECTRO-OPTICAL IMAGING TECHNOLOGY FOR UNMANNED AERIAL SYSTEM NAVIGATION IN A GPS-DENIED ENVIRONMENT

Navigation systems of unmanned aircraft systems (UAS) are heavily dependent on the availability of Global Positioning Systems (GPS) or other Global Navigation Satellite Systems (GNSS). Although inertial navigation systems (INS) can provide position and velocity of an aircraft based on acceleration measurements, the information degrades over time and reduces the capability of the system. In a GPS-denied environment, a UAS must utilize alternative sensor sources for navigating. This thesis presents preliminary evaluation results on the usage of onboard down-looking electro-optical sensors and image matching techniques to assist in GPS-free navigation of aerial platforms. Following the presentation of the fundamental mathematics behind the proposed concept, the thesis analyzes the key results from three flight campaign experiments that use different sets of sensors to collect data. Each of the flight experiments explores different sensor setups, assesses a variety of image processing methods, looks at different terrain environments, and reveals limitations related to the proposed approach. In addition, an attempt to incorporate navigational aid solutions into a navigation system using a Kalman filter is demonstrated. The thesis concludes with recommendations for future research on developing an integrated navigation system that relies on inertial measurement unit data complemented by the positional fixes from the image-matching technique.Outstanding ThesisCivilian, DSO National Laboratories, SingaporeApproved for public release. Distribution is unlimited

Big data analytics and processing for urban surveillance systems

Urban surveillance systems will be more demanding in the future towards smart city to improve the intelligence of cities. Big data analytics and processing for urban surveillance systems become increasingly important research areas because of infinite generation of massive data volumes all over the world. This thesis focused on solving several challenging big data issues in urban surveillance systems. First, we proposed several simple yet efficient video data recoding algorithms to be used in urban surveillance systems. The key idea is to record the important video frames when cutting the number of unimportant video frames. Second, since the DCT based JPEG standard encounters problems such as block artifacts, we proposed a very simple but effective method which results in better quality than widely used filters while consuming much less computer CPU resources. Third, we designed a novel filter to detect either the vehicle license plates or the vehicles from the images captured by the digital camera imaging sensors. We are the first to design this kind of filter to detect the vehicle/license plate objects. Fourth, we proposed novel grate filter to identify whether there are objects in these images captured by the cameras. In this way the background images can be updated from time to time when no object is detected. Finally, we combined image hash with our novel density scan method to solve the problem of retrieving similar duplicate images

Real-time person re-identification for interactive environments

The work presented in this thesis was motivated by a vision of the future in which intelligent environments in public spaces such as galleries and museums, deliver useful and personalised services to people via natural interaction, that is, without the need for people to provide explicit instructions via tangible interfaces. Delivering the right services to the right people requires a means of biometrically identifying individuals and then re-identifying them as they move freely through the environment. Delivering the service they desire requires sensing their context, for example, sensing their location or proximity to resources. This thesis presents both a context-aware system and a person re-identification method. A tabletop display was designed and prototyped with an infrared person-sensing context function. In experimental evaluation it exhibited tracking performance comparable to other more complex systems. A real-time, viewpoint invariant, person re-identification method is proposed based on a novel set of Viewpoint Invariant Multi-modal (ViMM) feature descriptors collected from depth-sensing cameras. The method uses colour and a combination of anthropometric properties logged as a function of body orientation. A neural network classifier is used to perform re-identification

Intelligent video surveillance

In the focus of this thesis are the new and modified algorithms for object detection, recognition and tracking within the context of video analytics. The manual video surveillance has been proven to have low effectiveness and, at the same time, high expense because of the need in manual labour of operators, which are additionally prone to erroneous decisions. Along with increase of the number of surveillance cameras, there is a strong need to push for automatisation of the video analytics. The benefits of this approach can be found both in military and civilian applications. For military applications, it can help in localisation and tracking of objects of interest. For civilian applications, the similar object localisation procedures can make the criminal investigations more effective, extracting the meaningful data from the massive video footage. Recently, the wide accessibility of consumer unmanned aerial vehicles has become a new threat as even the simplest and cheapest airborne vessels can carry some cargo that means they can be upgraded to a serious weapon. Additionally they can be used for spying that imposes a threat to a private life. The autonomous car driving systems are now impossible without applying machine vision methods. The industrial applications require automatic quality control, including non-destructive methods and particularly methods based on the video analysis. All these applications give a strong evidence in a practical need in machine vision algorithms for object detection, tracking and classification and gave a reason for writing this thesis. The contributions to knowledge of the thesis consist of two main parts: video tracking and object detection and recognition, unified by the common idea of its applicability to video analytics problems. The novel algorithms for object detection and tracking, described in this thesis, are unsupervised and have only a small number of parameters. The approach is based on rigid motion segmentation by Bayesian filtering. The Bayesian filter, which was proposed specially for this method and contributes to its novelty, is formulated as a generic approach, and then applied to the video analytics problems. The method is augmented with optional object coordinate estimation using plain two-dimensional terrain assumption which gives a basis for the algorithm usage inside larger sensor data fusion models. The proposed approach for object detection and classification is based on the evolving systems concept and the new Typicality-Eccentricity Data Analytics (TEDA) framework. The methods are capable of solving classical problems of data mining: clustering, classification, and regression. The methods are proposed in a domain-independent way and are capable of addressing shift and drift of the data streams. Examples are given for the clustering and classification of the imagery data. For all the developed algorithms, the experiments have shown sustainable results on the testing data. The practical applications of the proposed algorithms are carefully examined and tested

Boosting for Generic 2D/3D Object Recognition

Generic object recognition is an important function of the human visual system. For an artificial vision system to be able to emulate the human perception abilities, it should also be able to perform generic object recognition. In this thesis, we address the generic object recognition problem and present different approaches and models which tackle different aspects of this difficult problem. First, we present a model for generic 2D object recognition from complex 2D images. The model exploits only appearance-based information, in the form of a combination of texture and color cues, for binary classification of 2D object classes. Learning is accomplished in a weakly supervised manner using Boosting. However, we live in a 3D world and the ability to recognize 3D objects is very important for any vision system. Therefore, we present a model for generic recognition of 3D objects from range images. Our model makes use of a combination of simple local shape descriptors extracted from range images for recognizing 3D object categories, as shape is an important information provided by range images. Moreover, we present a novel dataset for generic object recognition that provides 2D and range images about different object classes using a Time-of-Flight (ToF) camera. As the surrounding world contains thousands of different object categories, recognizing many different object classes is important as well. Therefore, we extend our generic 3D object recognition model to deal with the multi-class learning and recognition task. Moreover, we extend the multi-class recognition model by introducing a novel model which uses a combination of appearance-based information extracted from 2D images and range-based (shape) information extracted from range images for multi-class generic 3D object recognition and promising results are obtained