653 research outputs found
EGO-TOPO: Environment Affordances from Egocentric Video
First-person video naturally brings the use of a physical environment to the
forefront, since it shows the camera wearer interacting fluidly in a space
based on his intentions. However, current methods largely separate the observed
actions from the persistent space itself. We introduce a model for environment
affordances that is learned directly from egocentric video. The main idea is to
gain a human-centric model of a physical space (such as a kitchen) that
captures (1) the primary spatial zones of interaction and (2) the likely
activities they support. Our approach decomposes a space into a topological map
derived from first-person activity, organizing an ego-video into a series of
visits to the different zones. Further, we show how to link zones across
multiple related environments (e.g., from videos of multiple kitchens) to
obtain a consolidated representation of environment functionality. On
EPIC-Kitchens and EGTEA+, we demonstrate our approach for learning scene
affordances and anticipating future actions in long-form video.Comment: Published in CVPR 2020, project page:
http://vision.cs.utexas.edu/projects/ego-topo
Multiple Trajectory Prediction of Moving Agents with Memory Augmented Networks
Pedestrians and drivers are expected to safely navigate complex urban environments along with several non cooperating agents. Autonomous vehicles will soon replicate this capability. Each agent acquires a representation of the world from an egocentric perspective and must make decisions ensuring safety for itself and others. This requires to predict motion patterns of observed agents for a far enough future. In this paper we propose MANTRA, a model that exploits memory augmented networks to effectively predict multiple trajectories of other agents, observed from an egocentric perspective. Our model stores observations in memory and uses trained controllers to write meaningful pattern encodings and read trajectories that are most likely to occur in future. We show that our method is able to natively perform multi-modal trajectory prediction obtaining state-of-the art results on four datasets. Moreover, thanks to the non-parametric nature of the memory module, we show how once trained our system can continuously improve by ingesting novel patterns
Integration of Experts' and Beginners' Machine Operation Experiences to Obtain a Detailed Task Model
We propose a novel framework for integrating beginners' machine operational experiences with those of experts' to obtain a detailed task model. Beginners can provide valuable information for operation guidance and task design; for example, from the operations that are easy or difficult for them, the mistakes they make, and the strategy they tend to choose. However, beginners' experiences often vary widely and are difficult to integrate directly. Thus, we consider an operational experience as a sequence of hand-machine interactions at hotspots. Then, a few experts' experiences and a sufficient number of beginners' experiences are unified using two aggregation steps that align and integrate sequences of interactions. We applied our method to more than 40 experiences of a sewing task. The results demonstrate good potential for modeling and obtaining important properties of the task
EgoEnv: Human-centric environment representations from egocentric video
First-person video highlights a camera-wearer's activities in the context of
their persistent environment. However, current video understanding approaches
reason over visual features from short video clips that are detached from the
underlying physical space and capture only what is immediately visible. To
facilitate human-centric environment understanding, we present an approach that
links egocentric video and the environment by learning representations that are
predictive of the camera-wearer's (potentially unseen) local surroundings. We
train such models using videos from agents in simulated 3D environments where
the environment is fully observable, and test them on human-captured real-world
videos from unseen environments. On two human-centric video tasks, we show that
models equipped with our environment-aware features consistently outperform
their counterparts with traditional clip features. Moreover, despite being
trained exclusively on simulated videos, our approach successfully handles
real-world videos from HouseTours and Ego4D, and achieves state-of-the-art
results on the Ego4D NLQ challenge. Project page:
https://vision.cs.utexas.edu/projects/ego-env/Comment: Published in NeurIPS 2023 (Oral
A Survey on Human-aware Robot Navigation
Intelligent systems are increasingly part of our everyday lives and have been
integrated seamlessly to the point where it is difficult to imagine a world
without them. Physical manifestations of those systems on the other hand, in
the form of embodied agents or robots, have so far been used only for specific
applications and are often limited to functional roles (e.g. in the industry,
entertainment and military fields). Given the current growth and innovation in
the research communities concerned with the topics of robot navigation,
human-robot-interaction and human activity recognition, it seems like this
might soon change. Robots are increasingly easy to obtain and use and the
acceptance of them in general is growing. However, the design of a socially
compliant robot that can function as a companion needs to take various areas of
research into account. This paper is concerned with the navigation aspect of a
socially-compliant robot and provides a survey of existing solutions for the
relevant areas of research as well as an outlook on possible future directions.Comment: Robotics and Autonomous Systems, 202
Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia
International audienceThis paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach
MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain
Wearable cameras allow to acquire images and videos from the user's
perspective. These data can be processed to understand humans behavior. Despite
human behavior analysis has been thoroughly investigated in third person
vision, it is still understudied in egocentric settings and in particular in
industrial scenarios. To encourage research in this field, we present MECCANO,
a multimodal dataset of egocentric videos to study humans behavior
understanding in industrial-like settings. The multimodality is characterized
by the presence of gaze signals, depth maps and RGB videos acquired
simultaneously with a custom headset. The dataset has been explicitly labeled
for fundamental tasks in the context of human behavior understanding from a
first person view, such as recognizing and anticipating human-object
interactions. With the MECCANO dataset, we explored five different tasks
including 1) Action Recognition, 2) Active Objects Detection and Recognition,
3) Egocentric Human-Objects Interaction Detection, 4) Action Anticipation and
5) Next-Active Objects Detection. We propose a benchmark aimed to study human
behavior in the considered industrial-like scenario which demonstrates that the
investigated tasks and the considered scenario are challenging for
state-of-the-art algorithms. To support research in this field, we publicy
release the dataset at https://iplab.dmi.unict.it/MECCANO/.Comment: arXiv admin note: text overlap with arXiv:2010.0565
Cooktop Sensing Based on a YOLO Object Detection Algorithm
Deep Learning (DL) has provided a significant breakthrough in many areas of research and industry. The development of Convolutional Neural Networks (CNNs) has enabled the improvement of computer vision-based techniques, making the information gathered from cameras more useful. For this reason, recently, studies have been carried out on the use of image-based DL in some areas of people’s daily life. In this paper, an object detection-based algorithm is proposed to modify and improve the user experience in relation to the use of cooking appliances. The algorithm can sense common kitchen objects and identify interesting situations for users. Some of these situations are the detection of utensils on lit hobs, recognition of boiling, smoking and oil in kitchenware, and determination of good cookware size adjustment, among others. In addition, the authors have achieved sensor fusion by using a cooker hob with Bluetooth connectivity, so it is possible to automatically interact with it via an external device such as a computer or a mobile phone. Our main contribution focuses on supporting people when they are cooking, controlling heaters, or alerting them with different types of alarms. To the best of our knowledge, this is the first time a YOLO algorithm has been used to control the cooktop by means of visual sensorization. Moreover, this research paper provides a comparison of the detection performance among different YOLO networks. Additionally, a dataset of more than 7500 images has been generated and multiple data augmentation techniques have been compared. The results show that YOLOv5s can successfully detect common kitchen objects with high accuracy and fast speed, and it can be employed for realistic cooking environment applications. Finally, multiple examples of the identification of interesting situations and how we act on the cooktop are presented.The current study has been sponsored by the Government of the Basque Country-ELKARTEK21/10 KK-2021/00014 (“Estudio de nuevas técnicas de inteligencia artificial basadas en Deep Learning dirigidas a la optimización de procesos industriales”) and ELKARTEK23-DEEPBASK (“Creación de nuevos algoritmos de aprendizaje profundo aplicado a la industria”) research programmes
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