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Measuring and visualizing social distancing using deep learning and 3D computer vision

Measuring and visualizing social distancing using deep learn...

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2020 aaai fall symposium on AI for Social Good, AI4SG 2020

作者： AbdulRahman, Bilal Zhu, Zhigang The CUNY Graduate Center New YorkNY United States The CUNY City College New YorkNY United States

Social Distancing has proved a necessary measure in controlling the spread of Coronavirus. The CDC (Center for disease control and prevention) in the United States recommends 6 feet as a safe distance between individuals. Therefore, a surveillance system capable of measuring distances between individuals can prove beneficial in limiting the spread. Video surveillance systems have already been introduced in various fields of our daily life to enhance security and protect individuals and sensitive infrastructure. In this work we present a way to use the existing video surveillance infrastructure to measure and monitor social distancing. We present a practical approach to monitor this distance using artificial intelligence and projective geometry techniques. Our approach, after initial setup, works in real-time requiring only a monocular surveillance camera feed. The proposed approach utilizes YOLO v4 neural network object detector for detecting pedestrians in the camera’s view. Projective transformation is used to localize the pedestrians on the ground. Finally, the real world distances between pedestrians is calculated and visualized with the right perspective and occlusion relations as if the distance marks are actually on the ground. All the implementation is in real-time, and is performed on python using the OPENCV libraries and the YOLO v4 neural net with pretrained weights. Experimental results are provided to validate our approach. The code of this work will be made publicly available at GitHub upon acceptance. Copyright © 2020 for this paper by its authors.

关键词： Cameras

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A framework using machine vision and deep reinforcement learning for self-learning moving objects in a virtual environment

A framework using machine vision and deep reinforcement lear...

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2017 aaai fall symposium

作者： Wu, Richard Zhao, Ying Clarke, Alan Kendall, Anthony Naval Postgraduate School CA United States

ISBN: (纸本)9781577357940

In recent artificial intelligence (AI) research, convolutional neural networks (CNNs) can create artificial agents capable of self-learning. Self-learning autonomous moving objects utilize machine vision techniques based on processing and recognizing objects in digital images. Afterwards, deep reinforcement learning (Deep-RL) is applied to understand and learn intelligent actions and controls. The objective of our research is to study methods and designs on how machine vision and deep machine learning algorithms can be implemented in a virtual world (e.g., a computer game) for moving objects (e.g., vehicles or aircrafts) to improve their navigation and detection of threats in real life. In this paper, we create a framework for generating and using data from computer games to be used in CNNs and Deep-RL to perform intelligent actions. We show the initial results of applying the framework and identify various military applications that may benefit from this research. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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Robot nonverbal communication as an ai problem (And Solution)

Robot nonverbal communication as an ai problem (And Solution...

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aaai 2015 fall symposium

作者： Admoni, Henny Scassellati, Brian Department of Computer Science Yale University New HavenCT06520 United States

ISBN: (纸本)9781577357476

In typical human interactions, nonverbal behaviors such as eye gazes and gestures serve to augment and reinforce spoken communication. To use similar nonverbal behaviors in human-robot interactions, researchers can apply artificial intelligence techniques such as machine learning, cognitive modeling, and computer vision. But knowledge of nonverbal behavior can also benefit artificial intelligence: because nonverbal communication can reveal human mental states, these behaviors provide additional input to artificial intelligence problems such as learning from demonstration, natural language processing, and motion planning. This article describes how nonverbal communication in HRI can benefit from AI techniques as well as how AI problems can use nonverbal communication in their solutions. © Copyright 2015, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Motion planning

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Represent and infer human theory of mind for human-robot interaction

Represent and infer human theory of mind for human-robot int...

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aaai 2015 fall symposium

作者： Zhao, Yibiao Holtzen, Steven Gao, Tao Zhu, Song-Chun Center for Vision Cognition Learning and Autonomy University of California Los Angeles United States Brain and Cognitive Sciences United States

ISBN: (纸本)9781577357476

This abstract is proposing a challenging problem: To infer a human's mental state - intent and belief - from an observed RGBD video for human-robot interaction. The task is to integrate symbolic reasoning, a field well- studied within A.I. domains, with the uncertainty native to computer vision strategies. Traditional A.I. strategies for plan inference typically rely on first-order logic and closed world assumptions which struggle to take into account the inherent uncertainty of noisy observations within a scene. computer vision relies on pattern- recognition strategies that have difficulty accounting for higher-level reasoning and abstract representation of world knowledge. By combining these two approaches in a principled way under a probabilistic programming framework, we define new computer vision tasks such as actor intent prediction and belief inference from an observed video sequence. Through inferring a human's theory of mind, a robotic agent can automatically determine a human's goals to collaborate with them. © Copyright 2015, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Human robot interaction

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learning and detecting patterns in multi-attributed network data

Learning and detecting patterns in multi-attributed network ...

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2012 aaai fall symposium

作者： Levchuk, Georgiy Roberts, Jennifer Freeman, Jared Aptima Inc. 12 Gill Street Woburn MA 01801 United States

Network analysis is a growing field across many domains, including computer vision, social media marketing, transportation networks, and intelligence analysis. The growing use of digital communication devices and platforms, as well as persistent surveillance sensors, has resulted in explosion of the quantity of data and stretched the abilities of current technologies to process this data and draw meaningful conclusions. Current tools either require significant levels of manual intervention (e.g., to prepare the data, to define patterns, or to draw conclusions from data) or are unable to generalize to new data sources and analysis needs. In this paper, we present automated solutions to two major problems in network analysis: (a) finding patterns in the network data that contains high levels of noise and irrelevant information;and (b) learning repetitive patterns and dependencies between entities and attributes. Our modeling framework represents network data using multi-attributed graphs that can encode various discrete and continuous features and relationships between network entities. The pattern search and learning model is based on probabilistic multi-attributed graph matching, and implemented using distributed message passing algorithms. Our algorithms achieved high accuracy rates in learning and finding patterns in the data, are flexible to new domains and data types, and scale to large datasets using the Map-Reduce framework. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Message passing

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NOAH for wheelchair users with cognitive impairment: Navigation and obstacle avoidance help

NOAH for wheelchair users with cognitive impairment: Navigat...

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2008 aaai fall symposium

作者： Viswanathan, Pooja Mackworth, Alan Little, James Hoey, Jesse Mihailidis, Alex Department of Computer Science University of British Columbia Vancouver BC Canada School of Computing University of Dundee Dundee United Kingdom Department of Occupational Science and Occupational Therapy University of Toronto Toronto ON Canada

ISBN: (纸本)9781577353942

Cognitive impairments prevent older adults from using powered wheelchairs due to safety concerns, thus reducing their mobility and resulting in increased dependence on caregivers. An intelligent powered wheelchair is proposed to help restore mobility, while ensuring safety. Machine vision and learning techniques are described to help prevent collisions with obstacles, and provide reminders and navigation assistance through adaptive prompts.

关键词： Wheelchairs

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learning invariant sensory-motor transforms for fault-tolerant control of redundant robots

Learning invariant sensory-motor transforms for fault-tolera...

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2008 aaai fall symposium

作者： Srinivasa, Narayan Grossberg, Stephen HRL Laboratories LLC 3011 Malibu Canyon Road Malibu CA-90265 United States Boston University 677 Beacon Street Boston MA-02215 United States

ISBN: (纸本)9781577353966

This paper describes a self-organizing neural model that is capable of autonomously learning to control robots with redundant degrees of freedom. The self-organized learning process is inspired by a fundamental principle prevalent in biological systems: action perception cycles wherein self-generated movement commands activate correlated visual, spatial and motor information. These self-generated movements or motor babbling are used to learn an internal invariant coordinate transformation between vision and motor systems. This invariance is achieved by the model by exploiting redundancy in the degrees of freedom available to the robotic system to learn a sensory-motor transform that is robust to a wide range of perturbations and failures in both sensory and motor parameters. To demonstrate the generality of the neural model, the learning process was tested on three different redundant robot systems with three different functional goals. The first robot system was a computer model of three degrees of freedom robot arm whose goal was to learn to reach for targets in 2-D space using the self-organizing neural model. The motor babbling process enabled the learning of a transform between changes in joint angles of the robot arm to the changes in the perceived direction of movement of the robot end-effector. The second robot system was a computer model of a head-neck-eye robotic stereo platform whose goal was to learn to saccade to 3-D targets. The motor babbling process in this case enabled the learning of a transform between changes in joint angles of the head, neck and eye to the changes in the perceived direction of movements of a 3-D target in the stereo camera. The third robot system was a real hexapod robotic platform with eighteen degrees of freedom whose goal was to learn to move to 3D targets in the real world. In this case, the robot self-generated movements using a central pattern generator (CPG) and learned the transform between joint angle changes of the lim

关键词： Eye movements

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Why some machines may need qualia and how they can have them: Including a demanding new turing test for robot philosophers

Why some machines may need qualia and how they can have them...

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2007 aaai fall symposium

作者： Sloman, Aaron School of Computer Science University of Birmingham United Kingdom

ISBN: (纸本)9781577353461

This paper extends three decades of work arguing that instead of focusing only on (adult) human minds, we should study many kinds of minds, natural and artificial, and try to understand the space containing all of them, by studying what they do, how they do it, and how the natural ones can be emulated in synthetic minds. That requires: (a) understanding sets of requirements that are met by different sorts of minds, i.e. the niches that they occupy, (b) understanding the space of possible designs, and (c) understanding the complex and varied relationships between requirements and designs. Attempts to model or explain any particular phenomenon, such as vision, emotion, learning, language use, or consciousness lead to muddle and confusion unless they are placed in that broader context, in part because current ontologies for specifying and comparing designs are inconsistent and inadequate. A methodology for making progress is summarised and a novel requirement proposed for human-like philosophical robots, namely that a single generic design, in addition to meeting many other more familiar requirements, should be capable of developing different and opposed viewpoints regarding philosophical questions about consciousness, and the socalled hard problem. No designs proposed so far come close. Copyright © 2007, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Philosophical aspects

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A COSPAL subsystem: Solving a shape-sorter puzzle

A COSPAL subsystem: Solving a shape-sorter puzzle

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2005 aaai fall symposium

作者： Felsberg, Michael Forssén, Per-Erik Moe, Anders Granlund, Gösta Computer Vision Laboratory Department of Electrical Engineering Linköping University SE-581 83 Linköping Sweden

ISBN: (纸本)1577352513

To program a robot to solve a simple shape-sorter puzzle is trivial. To devise a Cognitive System Architecture, which allows the system to find out by itself how to go about a solution, is less than trivial. The development of such an architecture is one of the aims of the COSPAL project, leading to new techniques in vision based Artificial Cognitive Systems, which allow the development of robust systems for real dynamic environments. The systems developed under the project itself remain however in simplified scenarios, likewise the shape-sorter problem described in the present paper. The key property of the described system is its robustness. Since we apply association strategies of local features, the system behaves robustly under a wide range of distortions, as occlusion, colour and intensity changes. The segmentation step which is applied in many systems known from literature is replaced with local associations and view-based hypothesis validation. The hypotheses used in our system are based on the anticipated state of the visual percepts. This state replaces explicit modeling of shapes. The current state is chosen by a voting system and verified against the true visual percepts. The anticipated state is obtained from the association to the manipulator actions, where reinforcement learning replaces the explicit calculation of actions. These three differences to classical schemes allow the design of a much more generic and flexible system with a high level of robustness. On the technical side, the channel representation of information and associative learning in terms of the channel learning architecture are essential ingredients for the system. It is the properties of locality, smoothness, and non-negativity which make these techniques suitable for this kind of application. The paper gives brief descriptions of how different system parts have been implemented and show some examples from our tests. Copyright © 2005, American Association for Artificial Intelligenc

关键词： Robots

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Engines of the brain: The compntational "instruction set" of perception and cognition

Engines of the brain: The compntational "instruction set" of...

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2004 aaai fall symposium

作者： Granger, R. Petrovic, S. Feich, A. Kerr, J. Johnson, M. Wuerth, C. Beovenuto, J. Brain Engineering Laboratory Computer Sci and Cognitive Sci Depts University of California Irvine CA 92697

Cognition is the action and interaction of multiple brain regions, and these are becoming understood computationally. Simulation and analysis has led to derivation of a set of elemental operations that emerge from individual and combined brain circuits, such that each circuit contributes a particular algorithm, and pairs and larger groups interact to compose further algorithms. We forward the hypothesis that these operations constitute the "instruction set" of the brain, i.e., that these are the most basic mental operations, from which all other behavioral and cognitive operations are constructed, constructing a unified formalism for description of operations ranging from perceptual to cognitive, including vision, language, learning and reasoning.

关键词： Brain

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