Multi-modal Human Detection, Tracking and Analysis for Robots in Crowded Environments PDF Download
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Author: Timm Linder
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Timm Linder
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Bin Fang
Publisher: Frontiers Media SA
ISBN: 2889668444
Category : Science
Languages : en
Pages : 224
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Author: Xavier Alameda-Pineda
Publisher: Academic Press
ISBN: 0128146028
Category : Computers
Languages : en
Pages : 498
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Book Description
Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification), and high level (personality and emotion recognition), providing insights on how to exploit inter-level and intra-level links. This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild. It is suitable for researchers and graduate students in the fields of computer vision, audio processing, pattern recognition, machine learning and social signal processing. Gives a comprehensive collection of information on the state-of-the-art, limitations, and challenges associated with extracting behavioral cues from real-world scenarios Presents numerous applications on how different behavioral cues have been successfully extracted from different data sources Provides a wide variety of methodologies used to extract behavioral cues from multi-modal data
Author: Jordi Vitria
Publisher: Springer Science & Business Media
ISBN: 3642212565
Category : Computers
Languages : en
Pages : 773
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Book Description
This volume constitutes the refereed proceedings of the 5th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2011, held in Las Palmas de Gran Canaria, Spain, in June 2011. The 34 revised full papers and 58 revised poster papers presented were carefully reviewed and selected from 158 submissions. The papers are organized in topical sections on computer vision; image processing and analysis; medical applications; and pattern recognition.
Author: Angelique Taylor
Publisher:
ISBN:
Category :
Languages : en
Pages : 241
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Book Description
The field of robotics is growing at a rapid pace with robot deployments in everyday environments such as hospitals, schools, and retail settings. On average, 70% of people in these environments are in groups: they walk, work, and interact in groups. Recent work in the field has highlighted the importance of designing robots that can interact with groups. To enable robots to fluently assist and interact with groups, they need a high-level understanding of team dynamics, including how to sense groups and engage in intelligent decision making to support them. However, the human-robot interactions (HRI) field has focused on dyadic interaction (i.e., one humand and one robot) which does not represent real-world situations where robots might interact with any number of people at a given time. The goal of my Ph.D. research is to design robotic systems that enable robots to work seamlessly in teams in real-world, safety-critical settings. In this dissertation, I discuss four main contributions of my work. First, I designed the Robot-Centric Group Estimation model (RoboGEM), which enables robots to detect human groups in complex, real-world environments. Prior group perception work tends to: (1) focus on exo-centric perspective approaches, (2) use data captured in well-controlled environments which cannot support real-world operating scenarios, and (3) use supervised learning methods that may potentially fail when robots encounter new situations. In contrast, RoboGEM is unsupervised and works well on ego-centric, real-world data, where both pedestrians and the robot are in motion at the same time. RoboGEM outperforms the current top-performing method by 10% in terms of accuracy, and 50% in terms of recall, and it can be used in real-world environments to enable robots to work in teams. Second, I expanded the scope of RoboGEM to design RoboGEM 2.0 which enables a robot to track groups over time in crowded environments. RoboGEM 2.0 is based on the intuition that pedestrians are most likely in groups when they have similar trajectories, ground plane coordinates, and proximities. RoboGEM 2.0 leverages deep learning techniques for group data association which enables robots to track groups when ego-motion uncertainty is high. It includes new methods for group tracking that employ Convolutional Neural Network (CNN) feature maps for group data association, and Kalman filtering to track group states over time. I compared RoboGEM 2.0 to three state-of-the-art methods and showed that it outperforms them in terms of precision, recall, and tracking accuracy. Unlike prior methods that require multiple sensors and substantial computational resource, RoboGEM 2.0 enables robots to detect and track groups of people in real-time from a moving platform using a single RGB-D sensor. Third, I explored using RoboGEM within a real-world application: teaming in healthcare. This is a dynamic setting in which teams experience coordination, communication, and decision-making challenges, rendering it a well-suited application domain for my work. I was interested in how robots might be used to reduce the degree of preventable patient harm, which in the US, kill over 400,000 patients and injure 5 million patients annually in hospitals alone. Here, nurses are the primary advocate for patients and thus are uniquely positioned to identify and prevent patient harm. However, strict hierarchical structures and asymmetrical power dynamics between physicians and nurses often result in penalties for nurses who speak up to "stop the line" of behavior that causes medical errors. This inspired my work, which involved collaborating with nurses to envision how robots might empower and support them in clinical teams. For example, our study revealed that nurses want robots to assist with team decision-making, supply delivery, and team "choreography" during surgery and resuscitation procedures. This work provided exciting design concepts for future robot technology in acute settings, which inspired later work in my PhD. Fourth, I continued my investigation into how robots can support clinical teams by exploring the use of robots in the Emergency Department (ED). The ED is a safety-critical environment in which providers are overburdened, overworked, and have limited resources to do their jobs. To place robots in these complex spaces, robots need to understand many features of the environment in order to operate safely and effectively, including patient acuity to prevent robots from interrupting treatment. To address this, I developed the Safety-Critical Deep Q-Network (SafeDQN), a new reinforcement learning system that enables robots to socially navigate while taking patient level of acuity into account. The main contribution of this work is a new computational model of patient acuity to enable robots to socially navigate in the ED. I compared SafeDQN to three classic navigation methods, and found that SafeDQN generates the safest, quickest path in a simulated ED environment. Using SafeDQN, mobile robots can fetch and deliver supplies to ED staff in a manner that does not interrupt patient care, thereby less likely to cause patient harm. My Ph.D. research contributes to building real-time robotic systems that can work alongside people in real-world environments. My work enables robots to effectively identify groups, track them over time, and navigate and interact among them in safety-critical, real-world settings. This work will enable more robust, realistic HRI, and support safe operation of mobile robots in human-centered environments.
Author: Adriana Tapus
Publisher: Springer
ISBN: 3319255541
Category : Computers
Languages : en
Pages : 734
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Book Description
This book constitutes the refereed proceedings of the 7th International Conference on Social Robotics, ICSR 2015, held in Paris, France, in October 2015. The 70 revised full papers presented were carefully reviewed and selected from 126 submissions. The papers focus on the interaction between humans and robots and the integration of robots into our society and present innovative ideas and concepts, new discoveries and improvements, novel applications on the latest fundamental advances in the core technologies that form the backbone of social robotics, distinguished developmental projects, as well as seminal works in aesthetic design, ethics and philosophy, studies on social impact and influence pertaining to social robotics, and its interaction and communication with human beings and its social impact on our society.
Author: H.H. Alvarez Valera
Publisher: IOS Press
ISBN: 1643682873
Category : Computers
Languages : en
Pages : 396
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Book Description
The term Intelligent Environments (IEs) refers to physical spaces in which information and communication technologies are interwoven with sensing technologies, innovative user interfaces, robotics and artificial intelligence to create interactive spaces which increase the awareness and enhance the experience of those occupying them. The growing IE community is rooted in academia, but increasingly involves practitioners. It explores the core ideas of IEs as well as the factors necessary to make them a reality, such as energy efficiency, the computational constraints of edge devices and privacy issues. This book presents papers from Workshops held during the 18th International Conference on Intelligent Environments, IE2022, held as a hybrid conference in Biarritz, France, from 20 to 23 June 2022. The conference is now recognized as a major annual venue in the field of IE. It offers a truly international forum for the exchange of information and ideas, and welcomes contributions from all technically active regions of the planet. Included here are 35 papers from the 1st International Workshop on Sentiment Analysis and Emotion Recognition for Social Robots (SENTIRobots’22); 1st International Workshop on Edge AI for Smart Agriculture (EAISA’22); 2nd International Workshop on Artificial Intelligence and Machine Learning for Emerging Topics (ALLEGET’22); 11th International Workshop on the Reliability of Intelligent Environments (WoRIE’22); 2nd International Workshop on Self-Learning in Intelligent Environments (SeLIE’22); 5th Workshop on Citizen Centric Smart Cities Solutions (CCSCS’22); 11th International Workshop on Intelligent Environments Supporting Healthcare and Well-being (WISHWell’22) Exploring some of the latest research and developments in the field, the book will be of interest to all those working with intelligent environments and its associated technologies.
Author: Minkyu Kim (Ph. D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Future tasks for robots to support humans in complex, dynamic, or human-populated environments will increasingly require more awareness of human behavior to operate effectively and safety. Human-centered behaviors are behaviors that seek to improve the interaction between humans and robots. For example, predicting the awareness of humans on nearby robots allows robots to be more "respectful" to their surroundings when navigating near them. Or another example, predicting where humans are heading to or what rooms they might be occupying can allow robots to follow and find people more effectively. In order for robots to be safe one of the top priorities is to know if nearby humans or pedestrians are aware of their presence. As such, in this dissertation we develop techniques to detect the gaze of humans within the robot's field of view and use this sensing modality to reason about awareness. For example, if a person is looking directly at a robot for a few seconds we assume that the person is aware of the robot moving nearby. We exploit this capability to plan paths for robots that are both safe – i.e. robots stay away within a few meters if humans are not looking – and respectful – i.e. by staying away from unaware humans they don't interrupt the tasks that humans might be performing. These considerations are important for safe and socially respectful navigation of mobile robots in human environments. Another important behavior of mobile robots is their ability to follow humans around in a "human-like" manner. By human-like we mean that we use inspiration about how people follow others in cluttered and dynamic environments. For instance, when a group of friends goes to a sports event some members of the group might temporally get lost. Those people may use inference regarding the heading direction of the group or contextual information – e.g. the group said they would head to a particular location – in order to rejoin them. As such, we have incorporate inference methods for robots to predict heading trajectories of humans as well as using contextual information of possible human locations in order to follow or relocate human teammates. A third important topic towards human-centered mobile robot behaviors is their ability to locate missing people, objects, or buildings in urban areas. This kind of capability is important for applications such as search and rescue or finding people in the crowd to deliver items, for instance. We have extensively explored such capability both in indoor and outdoor environments. Due to the complexity of searching in large spaces with many rooms, alleys, roads, buildings, etc, we've taken the approach of using multiple robots to accomplish such tasks more quickly. For indoor setups we've employed multiple robots including small (but fast) quadrupedal robots in coordination with wheeled robots. For outdoor setups we've employed golf-cart sized wheeled robots in coordination with Clearpath robots to sweep environments in search of missing objects. Overall, this dissertation has sought to improve the interaction and safety between mobile robots and humans. Within this area that I call human-centered mobile navigation my contributions include: (1) the integration of human awareness of robots in the state and reward models for POMDP optimization to improve social navigation in human environments; (2) exploring robust and autonomous person-following capabilities using active search in the sense that robots aim to recover individual people when they disappear from their field of view using multi-modal sensing and predictions; (3) using prior knowledge related to contextual information for statistical inference of possible object locations in case of occlusions or objects going missing; (4) realizing heterogeneous coverage path planning algorithms using clustering and information-theoretic optimization techniques that are faster than the state-of-the-art; (5) implementing all of these methods in a variety of hardware including wheeled and quadrupedal robots as well as teams of multiple robots working together
Author: Roberto Cipolla
Publisher: Springer
ISBN: 3642128483
Category : Technology & Engineering
Languages : en
Pages : 362
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Book Description
Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and tutorials held during the first two editions of the school on topics such as Recognition, Registration and Reconstruction. The chapters provide an in-depth overview of these challenging areas with key references to the existing literature.
Author: Vittorio Murino
Publisher: Academic Press
ISBN: 0128092807
Category : Computers
Languages : en
Pages : 440
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Book Description
Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning. Presents the first book to cover the topic of modeling and analysis of groups in computer vision Discusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithms Focuses on group and crowd analysis metrics Discusses real industrial systems dealing with the problem of analyzing groups and crowds
