2,097 research outputs found

    Using interaction signatures to find and label chairs and floors

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    The use of interaction signatures to recognize objects without considering the object\u27s physical structure is discussed. Without object recognition, smart homes cannot make full use of video cameras because vision systems cannot provide object-related context to the human activities monitored. One important advantage of interaction signatures is that people frequently and repeatedly interact with household objects, so the system can build evidence for object locations and labels

    Combining image regions and human activity for indirect object recognition in indoor wide-angle views

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    Traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wideangle and low-detail views such as surveillance scenes. To address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. No shape analysis is necessary. The concept is dubbed \u27interaction signatures\u27, since the premise is that a human will interact with objects in ways characteristic of the function of that object - for example, a person sits in a chair and drinks from a cup. The human-centred approach means that recognition is possible in low-detail views and is largely invariant to the shape of objects within the same functional class. This paper implements a Bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human\u27s interactions with the objects. Experiments show that interaction signatures can successfully find and label objects in low-detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.<br /

    Object labelling from human action recognition

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    Reproduced with the kind permissions of the copyright owner. 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 redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Copyright: 2003, IEE

    Fire and Life Safety Evaluation of an Assisted Living and Memory Care Center

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    This culminating project has been submitted as part of the graduate program in Fire Protection Engineering at Cal Poly. It documents an Assisted Living and Memory Care Center’s compliance with applicable fire safety prescriptions contained in the 2019 California Building and Fire Codes (CBC and CFC). Performance-based methods incorporating deterministic design fires were then used to verify that the final building design and operating procedures met the life safety needs of its unique occupants. The building under analysis was a 45,000 sq. ft, two-story, 58-bed residential care facility for the elderly. Occupants were all 60 years or older without acute medical conditions but with potential mild to severe mobility, sensory, and cognitive impairments. The fire- resistance-rated light-frame wood structure, its compartmentalized interior layout, and its active fire protection systems were found to satisfy the code provisions adopted by the local authority having jurisdiction. These included plentiful egress and exit capacity, localized fire and smoke containment, early smoke detection, audible and visual notification at levels appropriate to the occupants, and complete quick-response sprinkler coverage for life and property protection. The priorities of the performance-based analysis were to check the adequacy of these code-compliant fire protection features, as well as to support housing accessibility and to inform staff training. These required realistic fire models to verify available safe egress times (ASETs), which were shorter for these residents than the general population due to their lower tolerances for heat and smoke exposure. Design fires took guidance from NFPA 101 Life Safety Code and the author’s research on the history of fatal care home fires. All fires were placed in residential wings using heat release data from calorimetry tests of residential furniture and mixed natural/ synthetic hydrocarbon contents in staff supply closets. Initial growth rates were between fast (0.0469 kW/s2) and ultrafast (0.1876 kW/s2), with peak heat release rates and embodied energies appropriate to the fuel packages but ultimately determined by ventilation conditions. Model results supported the existing building design but showed that additional fuel control, compartmentation, detection/ notification, and automatic suppression would strengthen care staff’s response to and management of fires. Specifically, all rooms that communicate with residential corridors should have smoke detection and be fitted with door self-closers, following the findings of Performance Design Fires ‘B’ and ‘C.’ Where clients are housed also impacts their fire safety, so their facility intake forms/ health assessments should be used to guide placement— per Performance Design Fire ‘A,’ Assisted Living residents with the greatest cognitive, sensory, and locomotion disabilities should be housed closest to the lobby to receive prompt aid and minimize burns and smoke inhalation. These vulnerabilities also mean that sprinkler protection should be designed following the more rigorous commercial NFPA 13 standard as opposed to low- rise residential NFPA 13R, which was demonstrated in Performance Design Fire ‘D.’ Performance Design Fire ‘A’ was a nighttime living room furniture fire typical of all 40 Assisted Living dwellings. The occupant was assumed to be sleeping in the bedroom and not intimate with ignition; they were also capable of self-evacuation. Their required safe egress time (RSET) included a delay in waking to their low-frequency smoke alarm and traversing their unit to the corridor door, which totaled two minutes. At this time, the visibility through smoke was well below what would normally be accepted for design. The gasses at six feet above finished floor in the egress path were already too hot to move through (120°C), so the evacuee had to stoop, crouch, or even crawl, depending on the effectiveness of the sprinkler suppression. Since the sprinkler did temper heat, the asphyxiant fractional effective dose for incapacitation (FEDtot = 0.1) became the limiting tenability criteria; an especially respiratory-sensitive evacuee who took longer to find their door would have been incapacitated at two and a half minutes, but staff was expected to intervene by then. The slim margin for human error suggests that this scenario would benefit from a probabilistic assessment that includes ignition and suppression. A deterministic solution would be to regulate the flame spread and heat release of the furniture that residents bring in or are provided with. In scenarios ‘B’ and ‘C,’ a mixed cellulose/ plastics design fire was placed in staff supply closets with doors open to the residential hallways in the Assisted Living and Memory Care wings. The door in Performance Design Fire ‘B’ was self-closing, so wedging it open represented an n = 1 managerial failure; the closet sprinkler was operational. The nighttime RSET of Assisted Living residents to reach an adjacent smoke compartment was three to four minutes, depending on their disability. The ASET was the time for the smoke layer to descend to six feet in the corridor, which was the only evacuation route. This occurred by a minute and a half for 44% of the dwelling units along the hallway, which was the earliest staff was expected to arrive and close the fire room door. Since visibility at the staff entrance to the corridor was below two meters, and required crouching or crawling to access the room, closing the fire room door was not a certainty. This scenario necessitated partial or full defend-in-place in the Assisted Living wing. A similar result was found for the Memory Care wing in Performance Design Fire ‘C.’ A faulty sprinkler was an n = 1 device failure in this scenario because the closet door was not required to be self-closing. Occupants with dementia/ MNCD were assumed to be incapable of self-evacuation, and an RSET was not calculated for full staff evacuation of the wing, but it would have been much longer than the minute and a half ASET it took for smoke to descend to six feet in most of the corridor. Performance Design Fire ‘D’ looked at ignition within a Memory Care dwelling and NFPA 13’s requirement for sprinklers in clothes closets, which goes beyond NFPA 13R. This model also assumed an n = 1 device failure of the sprinkler. In contrast with Design Fire ‘A,’ the RSET was the time it took for an attendant to rescue the fire room occupant. This was just over a minute; since the fire was shielded from the main room sprinkler by the closet door, the fire burned uncontrolled, and the heat became intolerable overhead (200°C) after a minute and a half. This slim margin for attendant error echoes the conclusions of Design Fire ‘A.’ A summary of ASETs versus RSETs and additional observations can be found in Chapter 11. Facility operator responsibilities, including fuel control, housekeeping, fire protection systems maintenance, and emergency preparedness plans, can be found in the fire safety plan in Chapter 12. These are primarily based on the requirements of the CFC and the findings of this report\u27s prescriptive and performance chapters

    Automated Semantic Content Extraction from Images

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    In this study, an automatic semantic segmentation and object recognition methodology is implemented which bridges the semantic gap between low level features of image content and high level conceptual meaning. Semantically understanding an image is essential in modeling autonomous robots, targeting customers in marketing or reverse engineering of building information modeling in the construction industry. To achieve an understanding of a room from a single image we proposed a new object recognition framework which has four major components: segmentation, scene detection, conceptual cueing and object recognition. The new segmentation methodology developed in this research extends Felzenswalb\u27s cost function to include new surface index and depth features as well as color, texture and normal features to overcome issues of occlusion and shadowing commonly found in images. Adding depth allows capturing new features for object recognition stage to achieve high accuracy compared to the current state of the art. The goal was to develop an approach to capture and label perceptually important regions which often reflect global representation and understanding of the image. We developed a system by using contextual and common sense information for improving object recognition and scene detection, and fused the information from scene and objects to reduce the level of uncertainty. This study in addition to improving segmentation, scene detection and object recognition, can be used in applications that require physical parsing of the image into objects, surfaces and their relations. The applications include robotics, social networking, intelligence and anti-terrorism efforts, criminal investigations and security, marketing, and building information modeling in the construction industry. In this dissertation a structural framework (ontology) is developed that generates text descriptions based on understanding of objects, structures and the attributes of an image

    Tech Mecca in Giudecca

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    In this paper, we introduce the organization SerenDPT and its recent acquisition of the Herion Complex, a former Venetian Convent. The goal of our project is to assist SerenDPT in developing Herion, now known as H3, into a startup factory to bring high paying positions to Venice for young adults. We accomplished this through document organization, event planning, website development, and interior design guideline creation. With the knowledge researched and interviewed from experts in the field, we outline what we completed to guide future projects and to help H3 become a successful startup factory
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