What will happen if the down eye sensor does not detect an object?
Answers from top 7 papers
More filters
Papers (7) | Insight |
---|---|
7 Citations | While the eye looks down, the localization effect needs to be improved further. |
23 Oct 2005 63 Citations | ViewPointer not only allows any object to become an eye contact sensing appliance, it also allows identification of users and transmission of data to the user through the object. |
06 Jul 2017 33 Citations | Grid-EYE sensor overcomes the limitation of PIR sensor by detecting the human at stationary position. |
32 Citations | Experimental results show that the system can detect an object with angular accuracy of better than 1 mrad, demonstrating the feasibility of the proposed compound eye system. |
The device may be used on an undrugged eye and does not interfere with normal visual tasks. | |
01 Nov 2011 39 Citations | Moreover it is capable to detect eye blinks as well as relative eye lid movement. |
01 Dec 2007 17 Citations | Results show that the proposed model can effectively detect eye-blinks. |
Related Questions
What is the problem of vehicle detection with surveillance camera?5 answersVehicle detection with surveillance cameras faces the problem of dense vehicle detection in rush hours due to severe overlapping. Existing object detection methods may fail in accurately detecting densely located vehicles during peak traffic hours. This is because the severe overlapping of vehicles makes it challenging for traditional methods to distinguish individual vehicles. To address this problem, a proposed solution involves embedding the deformable channel-wise column transformer (DCCT) into the current you only look once (YOLO)-v5l network with a novel asymmetric focal loss (AF loss). The proposed DCCT extracts occlusion information and guides the network to focus on the visible area of partially occluded vehicles, improving the detection and positioning accuracy of weak feature targets. This approach has shown promising results in accurately detecting densely located vehicles, even in real-time scenarios.
What are the general challenges in object detection, especially when dealing with small objects like tiny birds?5 answersObject detection, especially for small objects like tiny birds, poses several challenges. These challenges include the noisy, blurred, and less-informative image appearances of small objects, as well as limitations in imaging acquisition conditions such as small target size, viewpoint specificity, background complexity, and scale and orientation diversity. Additionally, detecting small objects in aerial images captured by remote sensors presents unique challenges, such as low resolution, complex backgrounds, and variations in scale and angle. High-resolution, complex backgrounds, and information loss during information propagation also contribute to the difficulty of small object detection. Furthermore, severe occlusion, variant scales, and the small size of the objects hinder the performance of small object detection. These challenges require the development of specialized techniques and optimization strategies to improve small object detection in various contexts.
How do sensor limitations affect the performance of robotic vision systems?5 answersSensor limitations have a significant impact on the performance of robotic vision systems. These limitations include factors such as occlusions, low accuracy, expensive components, and lack of scalability. Occlusions, caused by objects blocking the sensor's line of sight, pose a challenge for object detection and grasping. Existing tactile sensor solutions with high resolution often suffer from low accuracy, expensive components, or lack of scalability. Additionally, the computational capability of micro-air vehicles and micro-robots is limited, making it difficult to perform real-time image stabilization to compensate for egomotion and external effects. These limitations hinder the ability of robotic vision systems to accurately perceive and interact with the environment, impacting their overall performance.
What are the challenges in implementing object detection on microcontrollers?5 answersImplementing object detection on microcontrollers faces several challenges. One major challenge is the limited hardware resources, including size, power consumption, and weight constraints, which make it difficult to run heavy machine learning models directly on microcontrollers. Another challenge is the high storage space requirement of modern machine learning models, such as deep neural networks (DNN), which cannot be accommodated by resource-constrained devices. To overcome these limitations, researchers have proposed innovative approaches, such as streaming video in real-time to a server and running object detection algorithms online, or using unique image pre-processing and feedforward artificial neural networks (ANN) to reduce the parameter size of the models. These approaches enable object detection on microcontrollers with limited storage space and low computational power, making it feasible to perform real-time inference with low energy consumption.
Can carbon monoxide cause a smoke detector to go off?14 answers
How do you know if a smoke detector is off?8 answers