453,618 research outputs found
Manoeuvring drone (Tello ans Tello EDU) using body poses or gestures
The project aims to use computer vision and machine learning to control Tello and Tello Edu Drones. The main use of this project is for educational and experimental purposes for upcoming future uses. I got inspired by the developments in the field of computer vision and pose estimation. As people are doing some much instructing and innovative things using this Drone and combined it with AI, Computer Vision, and Machine learning. This project helps anyone who likes to learn from the beginning even they donÂżt have any prior knowledge of programming or Computer Vision. They can able to build a simple application and also provide the basic knowledge for the development of a more complex application. As a final objective, the main application is proposed, are consists of a drone that can be controlled using body poses or the movement of the body i.e., following the person by maintaining a constant safety distance and following the commands given by the instructor. To start programming Tello or Tello EDU drone, it is based on a series of scripts and functions which is already been developed. So, that SDK is used as a guide to developing a new tello custom SDK to develop a complex and interactive application. By using this custom template, we can develop several basic mission scripts/programs. For the main application, different experiments were carried out to check which method is better in extracting the body key points/Landmarks in real-time in which low computing power is required or in other words no or less GPU power is required. As a result, I come across the Google AI open source ÂżMediaPipe Machine LearningÂż platform, which provides a cross-platform, customizable ML solution for live and streaming media. Which is used for advanced application in this project
How Creative Should Creators be to Optimize the Evolution of Ideas? A Computer Model
There are both benefits and drawbacks to creativity. In a social group it is not necessary for all members to be creative to benefit from creativity; some merely imitate or enjoy the fruits of others' creative efforts. What proportion should be creative? This paper outlines investigations of this question carried out using a computer model of cultural evolution referred to as EVOC (for EVOlution of Culture). EVOC is composed of neural network based agents that evolve fitter ideas for actions by (1) inventing new ideas through modification of existing ones, and (2) imitating neighbors' ideas. The ideal proportion with respect to fitness of ideas is found to depend on the level of creativity of the creative agents. For all levels or creativity, the diversity of ideas in a population is positively correlated with the ratio of creative agents
Cascaded 3D Full-body Pose Regression from Single Depth Image at 100 FPS
There are increasing real-time live applications in virtual reality, where it
plays an important role in capturing and retargetting 3D human pose. But it is
still challenging to estimate accurate 3D pose from consumer imaging devices
such as depth camera. This paper presents a novel cascaded 3D full-body pose
regression method to estimate accurate pose from a single depth image at 100
fps. The key idea is to train cascaded regressors based on Gradient Boosting
algorithm from pre-recorded human motion capture database. By incorporating
hierarchical kinematics model of human pose into the learning procedure, we can
directly estimate accurate 3D joint angles instead of joint positions. The
biggest advantage of this model is that the bone length can be preserved during
the whole 3D pose estimation procedure, which leads to more effective features
and higher pose estimation accuracy. Our method can be used as an
initialization procedure when combining with tracking methods. We demonstrate
the power of our method on a wide range of synthesized human motion data from
CMU mocap database, Human3.6M dataset and real human movements data captured in
real time. In our comparison against previous 3D pose estimation methods and
commercial system such as Kinect 2017, we achieve the state-of-the-art
accuracy
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
An Agent-based Simulation of the Effectiveness of Creative Leadership\ud
This paper investigates the effectiveness of creative versus\ud
uncreative leadership using EVOC, an agent-based model of\ud
cultural evolution. Each iteration, each agent in the artificial society invents a new action, or imitates a neighborâs action. Only the leaderâs actions can be imitated by all other agents, referred to as followers. Two measures of creativity were used: (1) invention-to-imitation ratio, iLeader, which measures how often an agent invents, and (2) rate of conceptual change, cLeader, which measures how creative an invention is. High iLeader increased mean fitness of ideas, but only when creativity of followers was low. High iLeader was associated with greater diversity of ideas in the early stage of idea generation only. High Leader increased mean fitness of ideas in the early stage of idea generation; in the later stage it decreased idea fitness. Reasons for these findings and tentative implications for creative leadership in human society are discussed
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Innovating Pedagogy 2015: Open University Innovation Report 4
This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation
Modeling Cultural Dynamics
EVOC (for EVOlution of Culture) is a computer model of culture that enables us to investigate how various factors such as barriers to cultural diffusion, the presence and choice of leaders, or changes in the ratio of innovation to imitation affect the diversity and effectiveness of ideas. It consists of neural network based agents that invent ideas for actions, and imitate neighborsâ actions. The model is based on a theory of culture according to which what evolves through culture is not memes or artifacts, but the internal models of the world that give rise to them, and they evolve not through a Darwinian process of competitive exclusion but a Lamarckian process involving exchange of innovation protocols. EVOC shows an increase in mean fitness of actions over time, and an increase and then decrease in the diversity of actions. Diversity of actions is positively correlated with population size and density, and with barriers between populations. Slowly eroding borders increase fitness without sacrificing diversity by fostering specialization followed by sharing of fit actions. Introducing a leader that broadcasts its actions throughout the population increases the fitness of actions but reduces diversity of actions. Increasing the number of leaders reduces this effect. Efforts are underway to simulate the conditions under which an agent immigrating from one culture to another contributes new ideas while still âfitting inâ
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