Elevator Object Detection
Elevator Object Detection - Related Videos
03:31
End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
0 Views •
2023
The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental contexts.
03:12
Freezing Point Depression and Boiling Point Elevation
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2020
Boiling Point Elevation The boiling point of a liquid is the temperature at which its vapor pressure is equal to ambient atmospheric pressure. Since the vapor pressure of a solution is lowered due to the presence of nonvolatile solutes, it stands to reason that the solution’s boiling point will subsequently be increased. Vapor pressure increases with temperature, and so a solution will require a higher temperature than will pure solvent to achieve any given vapor pressure, including one...
04:34
A Novel Non-invasive Method for the Detection of Elevated Intra-compartmental Pressures of the Leg
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Cited by 9 •
2019
An ultrasound probe coupled with a pressure sensor is used to assess the intra-compartmental pressure of the leg by directly measuring the compartment fascia flattening pressure (CFFP). This non-invasive protocol will provide reliable assessment of the pressure inside the anterior muscular compartment of the lower leg.
14:38
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Cited by 11 •
2012
We describe a novel methodology for creating naturalistic 3-D objects and object categories with precisely defined feature variations. We use simulations of the biological processes of morphogenesis and phylogenesis to create novel, naturalistic virtual 3-D objects and object categories that can then be rendered as visual images or haptic objects.
10:14
Object Substitution Masking
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Visual masking is a term used by perceptual scientists to refer to a wide range of phenomena in which in an image is presented but not perceived by an observer because of the presentation of a second image. There are several different kinds of masking, many of them relatively intuitive and unsurprising. But one surprising and important type of masking is called Object Substitution Masking. It has been a focus of research in vision...
FAQs
The system first processes camera images or video, then extracts visual features that support predictions of object classes and bounding boxes. A decision layer can evaluate whether a detected object enters a predefined region, such as a doorway area, or obstructs the doors. This connects visual recognition to responsive elevator behavior rather than treating detection as an isolated image-labeling task.
Lighting, occlusion, and camera placement are central performance variables. Poor or changing illumination can make visual features harder to interpret, while people or objects blocking one another can hide relevant evidence. Camera position also affects what portions of the cabin are visible. Engineering design must therefore balance visibility and coverage with processing speed and detection accuracy.
Object classes indicate what has been detected, while bounding boxes provide approximate locations within the camera view. Defined regions give those locations operational meaning, allowing the system to determine whether an object has entered an area associated with door obstruction or another response. This spatial reasoning helps translate recognition results into targeted engineering actions.
A basic workflow uses a camera to provide images or video to an object-detection model. The model extracts visual features, predicts object classes and bounding boxes, and passes those results to decision logic based on defined regions or door obstruction. Engineers then consider lighting, occlusion, camera placement, processing speed, accuracy, and privacy when integrating the output.
The approach can address people, packages, mobility devices, and other objects located inside or near elevator cabins. Its outputs may support door-obstruction prevention, occupancy monitoring, and more responsive operation. Because mobility devices and people can be distinguished as detected object categories, the same system can also contribute to accessibility-related functions when its visual results are reliable.
Detection outputs can support several engineering workflows beyond preventing door closure. Occupancy information may help characterize cabin use, while detected events can contribute to maintenance or security workflows. These applications still depend on the quality of visual detection and on appropriate handling of privacy. Engineers must weigh useful monitoring against accuracy, processing speed, camera conditions, and privacy requirements.