698,601 research outputs found
Deep HMResNet Model for Human Activity-Aware Robotic Systems
Endowing the robotic systems with cognitive capabilities for recognizing
daily activities of humans is an important challenge, which requires
sophisticated and novel approaches. Most of the proposed approaches explore
pattern recognition techniques which are generally based on hand-crafted
features or learned features. In this paper, a novel Hierarchal Multichannel
Deep Residual Network (HMResNet) model is proposed for robotic systems to
recognize daily human activities in the ambient environments. The introduced
model is comprised of multilevel fusion layers. The proposed Multichannel 1D
Deep Residual Network model is, at the features level, combined with a
Bottleneck MLP neural network to automatically extract robust features
regardless of the hardware configuration and, at the decision level, is fully
connected with an MLP neural network to recognize daily human activities.
Empirical experiments on real-world datasets and an online demonstration are
used for validating the proposed model. Results demonstrated that the proposed
model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606
Robot house human activity recognition dataset
© 2021 EPSRC UK-Robotics and Autonomous Systems (UK-RAS) Network. This is an open access conference paper distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Human activity recognition is one of the most challenging tasks in computer vision. State-of-the art approaches such as deep learning techniques thereby often rely on large labelled datasets of human activities. However, currently available datasets are suboptimal for learning human activities in companion robotics scenarios at home, for example, missing crucial perspectives. With this as a consideration, we present the University of Hertfordshire Robot House Human Activity Recognition Dataset (RH-HAR-1). It contains RGB videos of a human engaging in daily activities, taken from four different cameras. Importantly, this dataset contains two non-standard perspectives: a ceiling-mounted fisheye camera and a mobile robot's view. In the first instance, RH-HAR-1 covers five daily activities with a total of more than 10,000 videos.Human activity recognition is one of the most challenging tasks in computer vision. State-of-the art approaches such as deep learning techniques thereby often rely on large labelled datasets of human activities. However, currently available datasets are suboptimal for learning human activities in companion robotics scenarios at home, for example, missing crucial perspectives. With this as a consideration, we present the University of Hertfordshire Robot House Human Activity Recognition Dataset (RH-HAR-1). It contains RGB videos of a human engaging in daily activities, taken from four different cameras. Importantly, this dataset contains two non-standard perspectives: a ceiling-mounted fisheye camera and a mobile robot's view. In the first instance, RH-HAR-1 covers five daily activities with a total of more than 10,000 videos.Human activity recognition is one of the most challenging tasks in computer vision. State-of-the art approaches such as deep learning techniques thereby often rely on large labelled datasets of human activities. However, currently available datasets are suboptimal for learning human activities in companion robotics scenarios at home, for example, missing crucial perspectives. With this as a consideration, we present the University of Hertfordshire Robot House Human Activity Recognition Dataset (RH-HAR-1). It contains RGB videos of a human engaging in daily activities, taken from four different cameras. Importantly, this dataset contains two non-standard perspectives: a ceiling-mounted fisheye camera and a mobile robot's view. In the first instance, RH-HAR-1 covers five daily activities with a total of more than 10,000 videos
Detecting Hands in Egocentric Videos: Towards Action Recognition
Recently, there has been a growing interest in analyzing human daily
activities from data collected by wearable cameras. Since the hands are
involved in a vast set of daily tasks, detecting hands in egocentric images is
an important step towards the recognition of a variety of egocentric actions.
However, besides extreme illumination changes in egocentric images, hand
detection is not a trivial task because of the intrinsic large variability of
hand appearance. We propose a hand detector that exploits skin modeling for
fast hand proposal generation and Convolutional Neural Networks for hand
recognition. We tested our method on UNIGE-HANDS dataset and we showed that the
proposed approach achieves competitive hand detection results
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding
performance and demonstrated the effectiveness of 3D representation for action
recognition. The existing depth-based and RGB+D-based action recognition
benchmarks have a number of limitations, including the lack of large-scale
training samples, realistic number of distinct class categories, diversity in
camera views, varied environmental conditions, and variety of human subjects.
In this work, we introduce a large-scale dataset for RGB+D human action
recognition, which is collected from 106 distinct subjects and contains more
than 114 thousand video samples and 8 million frames. This dataset contains 120
different action classes including daily, mutual, and health-related
activities. We evaluate the performance of a series of existing 3D activity
analysis methods on this dataset, and show the advantage of applying deep
learning methods for 3D-based human action recognition. Furthermore, we
investigate a novel one-shot 3D activity recognition problem on our dataset,
and a simple yet effective Action-Part Semantic Relevance-aware (APSR)
framework is proposed for this task, which yields promising results for
recognition of the novel action classes. We believe the introduction of this
large-scale dataset will enable the community to apply, adapt, and develop
various data-hungry learning techniques for depth-based and RGB+D-based human
activity understanding. [The dataset is available at:
http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
HMM-based activity recognition with a ceiling RGB-D camera
Automated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, the abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available
Comparing the Performance of Machine Learning Algorithms for Human Activities Recognition using WISDM Dataset
Human activity recognition is an important area of machine learning research as it has much utilization in different areas such as sports training, security, entertainment, ambient-assisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Mobile phones are used to be more than luxury products, it has become a kind of urgent need for a fast-moving world with rapid development. Nowadays mobile phone is well equipped with advanced processor, more memory, powerful battery and built-in sensors. This provides an opportunity to open up new areas of data mining for activity recognition of human’s daily living. In this paper, we tested experiment using Tree based Classifiers (Decision Tree, J48, JRIP, and Random Forest) and Rule based algorithms Classifiers (Naive Bayes and AD1) to classify six activities of daily life by using Weka tool. According to the tested results Random Forest classifier is more accurate than other classifiers
A human computer interactions framework for biometric user identification
Computer assisted functionalities and services have saturated our world becoming such an integral part of our daily activities that we hardly notice them. In this study we are focusing on enhancements in Human-Computer Interaction (HCI) that can be achieved by natural user recognition embedded in the employed interaction models. Natural identification among humans is mostly based on biometric characteristics representing what-we-are (face, body outlook, voice, etc.) and how-we-behave (gait, gestures, posture, etc.) Following this observation, we investigate different approaches and methods for adapting existing biometric identification methods and technologies to the needs of evolving natural human computer interfaces
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
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