Unsupervised activity recognition using latent semantic analysis on a mobile robot
Paul Duckworth,Muhannad Al-Omari,Yiannis Gatsoulis,David C. Hogg,Anthony G. Cohn +4 more
- 29 Aug 2016
- pp 1062-1070
24
TL;DR: It is shown that the abstraction into a qualitative space helps the robot to generalise and compare multiple noisy and partial observations in a real world dataset and that a vocabulary of latent activity classes (expressed using qualitative features) can be recovered.
read more
Abstract: We show that by using qualitative spatio-temporal abstraction methods, we can learn common human movements and activities from long term observation by a mobile robot. Our novel framework encodes multiple qualitative abstractions of RGBD video from detected activities performed by a human as encoded by a skeleton pose estimator. Analogously to informational retrieval in text corpora, we use Latent Semantic Analysis (LSA) to uncover latent, semantically meaningful, concepts in an unsupervised manner, where the vocabulary is occurrences of qualitative spatio-temporal features extracted from video clips, and the discovered concepts are regarded as activity classes. The limited field of view of a mobile robot represents a particular challenge, owing to the obscured, partial and noisy human detections and skeleton pose-estimates from its environment. We show that the abstraction into a qualitative space helps the robot to generalise and compare multiple noisy and partial observations in a real world dataset and that a vocabulary of latent activity classes (expressed using qualitative features) can be recovered.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures
TL;DR: The testing results indicate that the proposed approach outperforms most of existing state-of-the-art methods, such as histogram of oriented 4-D normals and Actionlet on MSRAction3D.
Vision-based human action recognition: An overview and real world challenges
Imen Jegham,Anouar Ben Khalifa,Ihsen Alouani,Mohamed Ali Mahjoub +3 more
- 01 Mar 2020
TL;DR: This paper investigates an overview of the existing methods according to the kind of issue they address, and presents a comparison of the already introduced datasets introduced for the human action recognition field.
177
•Proceedings Article
Natural Language Acquisition and Grounding for Embodied Robotic Systems
Muhannad Al-Omari,Paul Duckworth,David C. Hogg,Anthony G. Cohn +3 more
- 12 Feb 2017
TL;DR: A cognitively plausible novel framework capable of learning the grounding in visual semantics and the grammar of natural language commands given to a robot in a table top environment and the knowledge learned is used to parse new commands involving previously unseen objects.
54
Multiple stream deep learning model for human action recognition
TL;DR: This paper uses multiple models to characterize both global and local motion features to characterize human action recognition, and shows the effectiveness of the proposed approach is comparable with the state-of-the-art.
44
•Proceedings Article
Latent Dirichlet Allocation for Unsupervised Activity Analysis on an Autonomous Mobile Robot
Paul Duckworth,Muhannad Al-Omari,James Charles,David C. Hogg,Anthony G. Cohn +4 more
- 13 Feb 2017
TL;DR: This work presents a method for unsupervised learning of common human movements and activities on an autonomous mobile robot, which generalises and improves on recent results and shows that the emergent categories align well with human activities as interpreted by a human.
15
References
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
•Proceedings Article
Latent Dirichlet Allocation
David M. Blei,Andrew Y. Ng,Michael I. Jordan +2 more
- 03 Jan 2001
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
•Proceedings Article
ROS: an open-source Robot Operating System
Morgan Quigley
- 01 Jan 2009
TL;DR: This paper discusses how ROS relates to existing robot software frameworks, and briefly overview some of the available application software which uses ROS.
10.2K
•Book
Probabilistic Robotics
Sebastian Thrun
- 01 Jan 2005
TL;DR: This research presents a novel approach to planning and navigation algorithms that exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles.
8.8K