A Multi-Resident Number Estimation Method for Smart Homes

Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%

Visual Human Tracking and Group Activity Analysis: A Video Mining System for Retail Marketing

Thesis (PhD) - Indiana University, Computer Sciences, 2007In this thesis we present a system for automatic human tracking and activity recognition from video sequences. The problem of automated analysis of visual information in order to derive descriptors of high level human activities has intrigued computer vision community for decades and is considered to be largely unsolved. A part of this interest is derived from the vast range of applications in which such a solution may be useful. We attempt to find efficient formulations of these tasks as applied to the extracting customer behavior information in a retail marketing context. Based on these formulations, we present a system that visually tracks customers in a retail store and performs a number of activity analysis tasks based on the output from the tracker. In tracking we introduce new techniques for pedestrian detection, initialization of the body model and a formulation of the temporal tracking as a global trans-dimensional optimization problem. Initial human detection is addressed by a novel method for head detection, which incorporates the knowledge of the camera projection model.The initialization of the human body model is addressed by newly developed shape and appearance descriptors. Temporal tracking of customer trajectories is performed by employing a human body tracking system designed as a Bayesian jump-diffusion filter. This approach demonstrates the ability to overcome model dimensionality ambiguities as people are leaving and entering the scene. Following the tracking, we developed a two-stage group activity formulation based upon the ideas from swarming research. For modeling purposes, all moving actors in the scene are viewed here as simplistic agents in the swarm. This allows to effectively define a set of inter-agent interactions, which combine to derive a distance metric used in further swarm clustering. This way, in the first stage the shoppers that belong to the same group are identified by deterministically clustering bodies to detect short term events and in the second stage events are post-processed to form clusters of group activities with fuzzy memberships. Quantitative analysis of the tracking subsystem shows an improvement over the state of the art methods, if used under similar conditions. Finally, based on the output from the tracker, the activity recognition procedure achieves over 80% correct shopper group detection, as validated by the human generated ground truth results

Innovative Wireless Localization Techniques and Applications

Innovative methodologies for the wireless localization of users and related applications are addressed in this thesis. In last years, the widespread diffusion of pervasive wireless communication (e.g., Wi-Fi) and global localization services (e.g., GPS) has boosted the interest and the research on location information and services. Location-aware applications are becoming fundamental to a growing number of consumers (e.g., navigation, advertising, seamless user interaction with smart places), private and public institutions in the fields of energy efficiency, security, safety, fleet management, emergency response. In this context, the position of the user - where is often more valuable for deploying services of interest than the identity of the user itself - who. In detail, opportunistic approaches based on the analysis of electromagnetic field indicators (i.e., received signal strength and channel state information) for the presence detection, the localization, the tracking and the posture recognition of cooperative and non-cooperative (device-free) users in indoor environments are proposed and validated in real world test sites. The methodologies are designed to exploit existing wireless infrastructures and commodity devices without any hardware modification. In outdoor environments, global positioning technologies are already available in commodity devices and vehicles, the research and knowledge transfer activities are actually focused on the design and validation of algorithms and systems devoted to support decision makers and operators for increasing efficiency, operations security, and management of large fleets as well as localized sensed information in order to gain situation awareness. In this field, a decision support system for emergency response and Civil Defense assets management (i.e., personnel and vehicles equipped with TETRA mobile radio) is described in terms of architecture and results of two-years of experimental validation

Extraction of Vehicle Groups in Airborne Lidar Point Clouds with Two-Level Point Processes

In this paper we present a new object based hierarchical model for joint probabilistic extraction of vehicles and groups of corresponding vehicles - called traffic segments - in airborne Lidar point clouds collected from dense urban areas. Firstly, the 3-D point set is classified into terrain, vehicle, roof, vegetation and clutter classes. Then the points with the corresponding class labels and echo strength (i.e. intensity) values are projected to the ground. In the obtained 2-D class and intensity maps we approximate the top view projections of vehicles by rectangles. Since our tasks are simultaneously the extraction of the rectangle population which describes the position, size and orientation of the vehicles and grouping the vehicles into the traffic segments, we propose a hierarchical, Two-Level Marked Point Process (L2MPP) model for the problem. The output vehicle and traffic segment configurations are extracted by an iterative stochastic optimization algorithm. We have tested the proposed method with real data of a discrete return Lidar sensor providing up to four range measurements for each laser pulse. Using manually annotated Ground Truth information on a data set containing 1009 vehicles, we provide quantitative evaluation results showing that the L2MPP model surpasses two earlier grid-based approaches, a 3-D point-cloud-based process and a single layer MPP solution. The accuracy of the proposed method measured in F-rate is 97% at object level, 83% at pixel level and 95% at group level

Fine-scale change detection using unmanned aircraft systems (UAS) to inform reproductive biology in nesting Waterbirds

Aerial photographic surveys from manned aircraft are commonly used to estimate the size of bird breeding colonies but are rarely used to evaluate reproductive success. Recent technological advances have spurred interest in the use of unmanned aircraft systems (UAS) for monitoring wildlife. The ability to repeatedly sample and collect imagery at fine-scale spatial and temporal resolutions while minimizing disturbance and safety risks make UAS particularly appealing for monitoring colonial nesting waterbirds. In addition, advances in photogrammetric and GIS software have allowed for more streamlined data processing and analysis. Using UAS imagery collected at Anaho Island National Wildlife Refuge during the peak of the nesting bird season, I evaluated the utility of UAS for monitoring and informing the reproductive biology of breeding American white pelicans (Pelecanus erythrorhynchos). By using a multitemporal nearest neighbor analysis for fine-scale change detection, I developed a novel, automated method to differentiate nesting from non-nesting individuals. All UAS images collected were of sufficient pixel resolution to differentiate adult pelicans from chicks, surrounding landscape features, and other species nesting on the island. No visual signs of disturbance due to the UAS were recorded. Pelican counts derived from UAS imagery were significantly higher than counts made from the ground at observation stations on the island. Analysis of multitemporal images provided more accurate classifications of nesting birds than did monotemporal images, on the condition that multitemporal images aligned with less than 0.5 m error. Nest classifications using multitemporal imagery were not significantly different when conducted across a 24 hour period compared to a 2 hour period. This technology shows promise for greatly enhancing the quality of colony monitoring data for large colonies and a species that is highly sensitive to disturbance

High-resolution sonar DF system

One of the fundamental problems of sonar systems is the determination of the bearings of underwater sources/targets. The classical solution to this problem, the 'Conventional Beamformer', uses the outputs from the individual sensors of an acoustic array to form a beam which is swept across the search sector. The resolution of this method is limited by the beam width and narrowing this beam to enhance the resolution may have some practical problems, especially in low frequency sonar, because of the physical size of the array needed. During the past two decades an enormous amount of work has been done to develop new algorithms for resolution enhancements beyond that of the Conventional Beamformer. However, most of these methods have been based on computer simulations and very little has been published on the practical implementation of these algorithms. One of the main reasons for this has been the lack of hardware that can handle the relatively heavy computational load of these algorithms. However, there have been great advances in semiconductor and computer technologies in the last few years which have led to the availability of more powerful computational and storage devices. These devices have opened the door to the possibility of implementing these high-resolution Direction Finding (DF) algorithms in real sonar systems. The work presented in this thesis describes a practical implementation of some of the high-resolution DF algorithms in a simple sonar system that has been designed and built for this purpose. [Continues.

Structure and antagonism of the receptor complex mediated by human TSLP in allergy and asthma

The pro-inflammatory cytokine thymic stromal lymphopoietin (TSLP) is pivotal to the pathophysiology of widespread allergic diseases mediated by type 2 helper T cell (Th2) responses, including asthma and atopic dermatitis. The emergence of human TSLP as a clinical target against asthma calls for maximally harnessing its therapeutic potential via structural and mechanistic considerations. Here we employ an integrative experimental approach focusing on productive and antagonized TSLP complexes and free cytokine. We reveal how cognate receptor TSLPR allosterically activates TSLP to potentiate the recruitment of the shared interleukin 7 receptor a-chain (IL-7Ra) by leveraging the flexibility, conformational heterogeneity and electrostatics of the cytokine. We further show that the monoclonal antibody Tezepelumab partly exploits these principles to neutralize TSLP activity. Finally, we introduce a fusion protein comprising a tandem of the TSLPR and IL-7Ra extracellular domains, which harnesses the mechanistic intricacies of the TSLP-driven receptor complex to manifest high antagonistic potency