A smart vision sensor for detecting risk factors of a toddler's fall in a home environment

This paper presents a smart vision sensor for detecting risk factors of a toddler's fall in an indoor home environment assisting parents' supervision to prevent fall injuries. We identified the risk factors by analyzing real fall injury stories and referring to a related organization's suggestions to prevent falls. In order to detect the risk factors using computer vision, two major image processing methods, clutter detection and toddler tracking, were studied with using only one commercial web-camera. For practical purposes, there is no need for a toddler to wear any sensors or markers. The algorithms for detection have been developed, implemented and tested

Vision-based toddler tracking at home

This paper presents a vision-based toddler tracking system for detecting risk factors of a toddler's fall within the home environment. The risk factors have environmental and behavioral aspects and the research in this paper focuses on the behavioral aspects. Apart from common image processing tasks such as background subtraction, the vision-based toddler tracking involves human classification, acquisition of motion and position information, and handling of regional merges and splits. The human classification is based on dynamic motion vectors of the human body. The center of mass of each contour is detected and connected with the closest center of mass in the next frame to obtain position, speed, and directional information. This tracking system is further enhanced by dealing with regional merges and splits due to multiple object occlusions. In order to identify the merges and splits, two directional detections of closest region centers are conducted between every two successive frames. Merges and splits of a single object due to errors in the background subtraction are also handled. The tracking algorithms have been developed, implemented and tested

A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.894244Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters (PFs) have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter. The PF also models image statistics such as mean and variance of the given data which can be useful in obtaining proper separation of object and backgroun

Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio

A comparative study of breast surface reconstruction for aesthetic outcome assessment

Breast cancer is the most prevalent cancer type in women, and while its survival rate is generally high the aesthetic outcome is an increasingly important factor when evaluating different treatment alternatives. 3D scanning and reconstruction techniques offer a flexible tool for building detailed and accurate 3D breast models that can be used both pre-operatively for surgical planning and post-operatively for aesthetic evaluation. This paper aims at comparing the accuracy of low-cost 3D scanning technologies with the significantly more expensive state-of-the-art 3D commercial scanners in the context of breast 3D reconstruction. We present results from 28 synthetic and clinical RGBD sequences, including 12 unique patients and an anthropomorphic phantom demonstrating the applicability of low-cost RGBD sensors to real clinical cases. Body deformation and homogeneous skin texture pose challenges to the studied reconstruction systems. Although these should be addressed appropriately if higher model quality is warranted, we observe that low-cost sensors are able to obtain valuable reconstructions comparable to the state-of-the-art within an error margin of 3 mm.Comment: This paper has been accepted to MICCAI201

2004 Coastal Conservation Outreach

The Seacoast Land Trust mission is to actively promote and effect the protection and stewardship of open lands in the Seacoast. Since our founding in 1998, programs and outings have been an essential part of the organizations activities. Through our programs we strive to raise the awareness about the importance of land conservation and land stewardship. The grant funding allowed us to offer a wide range of programs and activities and to focus on the importance of protection to our coastal habitats and wetland resources. The programs involved participants with a wide range of ages and interests and included concerned citizens, municipal and conservation leaders, students, and members of civic organizations. Highlights of these activities, their turnout and comments from participants are presented below. In all of these programs Seacoast Land Trust presented information on its current land protection programs and land conservation success stories as part of the activities. A presentation of mapping of the lands within the SLT service area, previously funded by the New Hampshire Estuaries Project and the New Hampshire Coastal Program was also displayed at each event. We ended up adding three additional programs and activities above the ten activities/programs originally proposed

Technology for Good: Innovative Use of Technology by Charities

Technology for Good identifies ten technologies being used by charitable organizations in innovative ways. The report briefly introduces each technology and provides examples of how those technologies are being used.Examples are drawn from a broad spectrum of organizations working on widely varied issues around the globe. This makes Technology for Good a unique repository of inspiration for the public and private sectors, funders, and other change makers who support the creation and use of technology for social good