Searching for complex human activities with no visual examples
Though we can change our pose, there are common regulations that limit how our pose can be and how it can move over time. Therefore, all types of human data will follow these rules and they can serve as prior knowledge or regularization in our learning framework. If we can exploit these tendencies, we are able to extract additional information from data and use them to improve learning of humans' actions and poses. In particular, we are able to find patterns for how our pose could vary over time, how our appearance looks in a specific view, how our pose is when we are interacting with objects with certain properties, and how part of our body configuration is shared across different poses.
If we could learn these patterns, they can be used to interconnect and extrapolate the knowledge between different data sources.
Artificial Intelligence and the Future of Humans | Pew Research Center
To this end, I propose several new ways to connect human activity data. First, I show how to connect snapshot images and videos by exploring the patterns of how our pose could change over time. Building on this idea, I explore how to connect humans' poses across multiple views by discovering the correlations between different poses and the latent factors that affect the viewpoint variations. In addition, I consider if there are also patterns connecting our poses and nearby objects when we are interacting with them.
Furthermore, I explore how we can utilize the predicted interaction as a cue to better address existing recognition problems including image re-targeting and image description generation. Finally, after learning models effectively incorporating these patterns, I propose a robust approach to efficiently localize when and where a complex action happens in a video sequence. The variants of my proposed approaches offer a good trade-off between computational cost and detection accuracy. My thesis exploits various types of underlying patterns in human data. See them all These key ideas relate to the causes and effects of human-induced climate change.
The human causes of climate change are some of the most important concepts to teach. Hide Caption Oil pump jack. Hide Caption Deforestation in Mexico. Hide Caption Carbon emissions have risen from about 2.
Human Activity Recognition Python
Find activities and visuals for teaching this topic Search by grade level: middle school high school intro college upper college search all grade levels. References National Climate Assessment Report summarizes the impacts of climate change on the United States, now and in the future. How do we know that recent CO 2 increases are due to human activities? Causes of Climate Change - This NASA web page describes the greenhouse effect, the role of human activity and the evidence that changes in solar irradiance are not related to recent temperature increases.
Global Warming's Six Americas - This ongoing project tracks Americans' opinions and beliefs about climate change.
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This approach identifies six unique audiences within the American public that each responds to the issue in their own distinct way. This is a great way to learn about the possible audiences among your student population. Doran, M. This article compares the consensus views of scientists and the general public on climate change.
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The Sun Provides Energy 2. Climate is Complex 3. Climate and Life 4. Climate is Variable 5. Understanding Climate 6. Humans Affect Climate 7. Acoustical waves, such as sounds and other forms of vibrations, are an alternative medium that may bypass those limitations. Unlike electromagnetic waves , such as those used in radar, acoustical waves can be used not only to find objects but also to identify them.
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- Climate Change Is Complex. We’ve Got Answers to Your Questions.;
- The Age of Humans: Evolutionary Perspectives on the Anthropocene?
- Searching Video for Complex Activities with Finite State Models - IEEE Conference Publication?
As described in a new paper in the May 28 issue of Applied Physics Letters , the researchers used a two-dimensional acoustic array and convolutional neural networks to detect and analyze the sounds of human activity and identify those activities. Thus, immediate help could be provided and with little privacy leaked at the same time. By using a two-dimensional acoustic array with receivers and four ultrasonic transmitters, the researchers were able to gather data related to four different human activities—sitting, standing, walking and falling.
They used a kilohertz signal to bypass any potential contamination from ordinary room noise and distinguish it from the identifying sounds. Their tests achieved an overall accuracy of The scientists also tested arrays with fewer receivers eight and four and found them to produce results with lower accuracy of the human activity.
Sound waves bypass visual limitations to recognize human activity
Guo said acoustic systems are a better detection device than vision-based systems because of the lack of widespread acceptance of cameras due to privacy issues. In addition, low lighting or smoke can also hamper vision recognition, but sound waves are not affected by those special environmental situations. We are hoping to collect more datasets of falling activity to reach higher accuracy.
Guo said they will be experimenting with various numbers of sensors and their effectiveness in detecting and determining human activities. He said there is an optimal number for the array that would make this viable for commercial and personal use in homes and buildings.
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