A Method for Detecting Long Term Left Baggage based on Heat Map

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Carried baggage detection and recognition in video surveillance with foreground segmentation

Security cameras installed in public spaces or in private organizations continuously record video data with the aim of detecting and preventing crime. For that reason, video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis, have gained high interest in recent years. In this thesis, the primary focus is on two key aspects of video analysis, reliable moving object segmentation and carried object detection & identification. A novel moving object segmentation scheme by background subtraction is presented in this thesis. The scheme relies on background modelling which is based on multi-directional gradient and phase congruency. As a post processing step, the detected foreground contours are refined by classifying the edge segments as either belonging to the foreground or background. Further contour completion technique by anisotropic diffusion is first introduced in this area. The proposed method targets cast shadow removal, gradual illumination change invariance, and closed contour extraction. A state of the art carried object detection method is employed as a benchmark algorithm. This method includes silhouette analysis by comparing human temporal templates with unencumbered human models. The implementation aspects of the algorithm are improved by automatically estimating the viewing direction of the pedestrian and are extended by a carried luggage identification module. As the temporal template is a frequency template and the information that it provides is not sufficient, a colour temporal template is introduced. The standard steps followed by the state of the art algorithm are approached from a different extended (by colour information) perspective, resulting in more accurate carried object segmentation. The experiments conducted in this research show that the proposed closed foreground segmentation technique attains all the aforementioned goals. The incremental improvements applied to the state of the art carried object detection algorithm revealed the full potential of the scheme. The experiments demonstrate the ability of the proposed carried object detection algorithm to supersede the state of the art method

Contextual anomaly detection in crowded surveillance scenes

AbstractThis work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour

Outside the fence,\u27 the threat to the U.S. Aviation Industry

The purpose of this thesis was to examine the threat to the Aviation Industry from within the United States. The overall investigation starts with one assumption “that there will at some time in the near future be a covert operative group that desires to attack or engage the United States in war on its home land.” The principles of war will be analyzed resulting in covert cell guidance specifically; “economy of force” will require the covert units to be as small as possible to affect as many nodes as possible. Endurance will require the covert team to restrict any tactics that would be high risk, and would prohibit the use of suicide tactics. There has also been a redefinition of warfare in the last several years. What has emerged is a form of unrestricted warfare. The covert cell may abide by the principles of war while engaging the U.S. in unrestricted warfare. These assumptions lead to a center of gravity determination and terrorism as the possible action for the desired effect. Attack and weapon selection analysis results in the selection of the 50 caliber sniper rifle with armor-piercing incendiary ammunition as the most probable attack tactic executed against urban airport environments. Possible solution analysis of acoustic, mid wave infrared and optical augmentation systems reveals the advantages of each of these approaches. The conclusion is that open system architecture should be used to tailor the sensor suite around each airport based on the vital area locations with respect to the urban layout and the best sniping positions. This will lead to a multi- layer and multi-system defensive posture around each airport significantly reducing the risk of a drawn out terror campaign which involves the airline industry

Preliminary design studies of an advanced general aviation aircraft

The preliminary design results are presented of the advanced aircraft design project. The goal was to take a revolutionary look into the design of a general aviation aircraft. Phase 1 of the project included the preliminary design of two configurations, a pusher, and a tractor. Phase 2 included the selection of only one configuration for further study. The pusher configuration was selected on the basis of performance characteristics, cabin noise, natural laminar flow, and system layouts. The design was then iterated to achieve higher levels of performance

Geometric Unified Method in 3D Object Classification

3D object classification is one of the most popular topics in the field of computer vision and computational geometry. Currently, the most popular state-of-the-art algorithm is the so-called Convolutional Neural Network (CNN) models with various representations that capture different features of the given 3D data, including voxels, local features, multi-view 2D features, and so on. With CNN as a holistic approach, researches focus on improving the accuracy and efficiency by designing the neural network architecture. This thesis aims to examine the existing work on 3D object classification and explore the underlying theory by integrating geometric approaches. By using geometric algorithms to pre-process and select data points, we dive into an existing architecture of directly feeding points into a deep CNN, and explore how geometry measures how important different points are in a CNN model. Moreover, we attempt to extract useful geometric features directly from the object data to introduce the feature matrix representation, which can be classified with distance-based approaches. We present all results of experiments and analyzed for future improvement

Downstream Task Self-Supervised Learning for Object Recognition and Tracking

This dissertation addresses three limitations of deep learning methods in image and video understanding-based machine vision applications. Firstly, although deep convolutional neural networks (CNNs) are efficient for image recognition applications such as object detection and segmentation, they perform poorly under perspective distortions. In real-world applications, the camera perspective is a common problem that we can address by annotating large amounts of data, thus limiting the applicability of the deep learning models. Secondly, the typical approach for single-camera tracking problems is to use separate motion and appearance models, which are expensive in terms of computations and training data requirements. Finally, conventional multi-camera video understanding techniques use supervised learning algorithms to determine temporal relationships among objects. In large-scale applications, these methods are also limited by the requirement of extensive manually annotated data and computational resources.To address these limitations, we develop an uncertainty-aware self-supervised learning (SSL) technique that captures a model\u27s instance or semantic segmentation uncertainty from overhead images and guides the model to learn the impact of the new perspective on object appearance. The test-time data augmentation-based pseudo-label refinement technique continuously trains a model until convergence on new perspective images. The proposed method can be applied for both self-supervision and semi-supervision, thus increasing the effectiveness of a deep pre-trained model in new domains. Extensive experiments demonstrate the effectiveness of the SSL technique in both object detection and semantic segmentation problems. In video understanding applications, we introduce simultaneous segmentation and tracking as an unsupervised spatio-temporal latent feature clustering problem. The jointly learned multi-task features leverage the task-dependent uncertainty to generate discriminative features in multi-object videos. Experiments have shown that the proposed tracker outperforms several state-of-the-art supervised methods. Finally, we proposed an unsupervised multi-camera tracklet association (MCTA) algorithm to track multiple objects in real-time. MCTA leverages the self-supervised detector model for single-camera tracking and solves the multi-camera tracking problem using multiple pair-wise camera associations modeled as a connected graph. The graph optimization method generates a global solution for partially or fully overlapping camera networks

Railcar Wheel Impact Detection Utilizing Vibration-Based Wireless Onboard Condition Monitoring Modules

The current limitations in established rail transport condition monitoring methods have motivated the UTCRS railway research team at UTRGV to investigate a novel solution that can address these deficiencies through wired, onboard, and vibration-based analytics. Due to the emergence of the Internet of Things (IoT), the research team has now transitioned into developing wireless modules that take advantage of the rapid data processing and wireless communication features these systems possess. This has enabled UTCRS to partner with Hum Industrial Technology, Inc. to assist them in the development of their “Boomerang” wireless condition monitoring system. Designed to revolutionize the way the railway industry monitors rolling stock assets; the product is intended to provide preemptive maintenance scheduling through the continuous monitoring of railcar wheels and bearings. Ultimately, customers can save time, money, and avoid potentially catastrophic events. The wheel condition monitoring capabilities of the Boomerang were evaluated through various laboratory experiments that mimicked rail service operating conditions. The possible optimization of the system by incorporating a filter was also investigated. To further validate the efficacy of the prototype, a pilot field test consisting of 40 modules was conducted. The exhibited agreement between the laboratory and field pilot test data as well as the detection of a faulty wheelset demonstrates the functionality of the sensor module as a railcar wheel health monitoring device