Results 321 to 330 of about 2,834,648 (359)

Human action recognition using two-stream attention based LSTM networks

Applied Soft Computing, 2020
It is well known that different frames play different roles in feature learning in video based human action recognition task. However, most existing deep learning models put the same weights on different visual and temporal cues in the parameter training
Cheng Dai, Xingang Liu, Jinfeng Lai
semanticscholar   +1 more source

Biological inspired human action recognition

2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS), 2013
Computational neuroscience studies through Functional magnetic resonance imaging (fMRI) claimed that human action recognition in the brain of mammalian pursues two separated pathways in the model, which are specialized for the analysis of motion (optic flow) and form information[3].
Bardia Yousefi, Chu Kiong Loo, Ali Memariani   +2 more
openaire   +1 more source

Active classification for human action recognition

2013 IEEE International Conference on Image Processing, 2013
In this paper, we propose a novel classification method involving two processing steps. Given a test sample, the training data residing to its neighborhood are determined. Classification is performed by a Single-hidden Layer Feedforward Neural network exploiting labeling information of the training data appearing in the test sample neighborhood and ...
Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas   +2 more
openaire   +1 more source

Tangent bundle for human action recognition

Face and Gesture 2011, 2011
Common human actions are instantly recognizable by people and increasingly machines need to understand this language if they are to engage smoothly with people. Here we introduce a new method for automated human action recognition. The proposed method represents videos as a tangent bundle on a Grassmann manifold.
Yui Man Lui, J. Ross Beveridge
openaire   +1 more source

Vision-based human action recognition: An overview and real world challenges

Digital Investigation. The International Journal of Digital Forensics and Incident Response, 2020
Within a large range of applications in computer vision, Human Action Recognition has become one of the most attractive research fields. Ambiguities in recognizing actions does not only come from the difficulty to define the motion of body parts, but ...
Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, M. Mahjoub   +3 more
semanticscholar   +1 more source

Human Action Recognition with Attribute Regularization

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship ...
Zhong Zhang 0001, Chunheng Wang, Baihua Xiao, Wen Zhou 0002, Shuang Liu 0001   +4 more
openaire   +1 more source

Correlational Convolutional LSTM for human action recognition

Neurocomputing, 2020
In light of recent exponential growth of video data, the need for automated video processing has increased substantially. To learn the intrinsic structure of video data, many representation approaches have been proposed, focusing on learning the spatial ...
M. Majd, R. Safabakhsh
semanticscholar   +1 more source

A Review of Human Action Recognition in Video

2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), 2018
Human action recognition has a strong theoretical significance and wide application prospect in the field of video surveillance, human-computer interaction, virtual reality, etc. In this paper we analyze the present research situation from the datasets, action feature extraction, action recognition and classification.
Nenghuan Zhang, Yongbin Wang, Peng Yu
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Human Action Recognition for Disaster Detection

2020 29th International Conference on Computer Communications and Networks (ICCCN), 2020
In this paper, in case of a panic condition in public indoor space due to fire, earthquake, terror, etc., we propose a disaster detection system that detects disaster quickly by analyzing people's behavior using deep learning model. Four types of human behavior were collected as acceleration sensor data: walking, running, going to eat after class, and ...
Yull Kyu Han, Young Bok Choi
openaire   +1 more source

PoHMM-based human action recognition

2009 10th Workshop on Image Analysis for Multimedia Interactive Services, 2009
In this paper we approach the human action recognition task using the Product of Hidden Markov Models (PoHMM). This approach allow us to get large state-space models from the normalized product of several simple HMMs. We compare this mixed graphical model with other directed multi-chain models like Coupled Hidden Markov Model (CHMM) or Factorial Hidden
Maria Ángeles Mendoza, Nicolas Pérez de la Blanca, Manuel J. Marín-Jiménez   +2 more
openaire   +1 more source