Corpus ID: 236447678

Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System

  title={Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System},
  author={Sahar Darafsh and Saeed Shiry Ghidary and Morteza Saheb Zamani},
With the rapid increase in digital technologies, most fields of study include recognition of human activity and intention recognition, which are important in smart environments. In this research, we introduce a real-time activity recognition to recognize people’s intentions to pass or not pass a door. This system, if applied in elevators and automatic doors will save energy and increase efficiency. For this study, data preparation is applied to combine the spatial and temporal features with the… Expand


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A user-independent deep learning-based approach for online human activity classification using Convolutional Neural Networks for local feature extraction together with simple statistical features that preserve information about the global form of time series is presented. Expand
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To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition and outperforms the existing methods on the KTH and WEIZMANN datasets. Expand
Human activity recognition in egocentric video using HOG, GiST and color features
A novel approach for human activity recognition in egocentric video has been invoked and it is demonstrated that the Random Forest classifier outperformed the SVM classifier. Expand
A Hybrid Framework for Action Recognition in Low-Quality Video Sequences
A hybrid model for illumination invariant human activity recognition based on sub-image histogram equalization enhancement and k-key pose human silhouettes is proposed, which outperformed on low exposure videos over existing technique and achieved comparable classification accuracy to similar state-of-the-art methods. Expand
ReHAR: Robust and Efficient Human Activity Recognition
  • Xin Li, M. Chuah
  • Computer Science
  • 2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
  • 2018
A novel robust and efficient human activity recognition scheme called ReHAR which can be used to handle single person activities and group activities prediction and achieves a higher activity recognition accuracy with an order of magnitude shorter computation time compared to the state-of-the-art methods. Expand
Vision-based human action recognition using machine learning techniques
This thesis addresses one of the major challenges known as “viewpoint variations” by presenting a novel feature descriptor for multiview human action recognition by embedding novel algorithms for action recognition, both in the handcrafted and deep learning domains. Expand
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
A novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network that is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval. Expand
Deep convolutional framework for abnormal behavior detection in a smart surveillance system
  • K. Ko, K. Sim
  • Computer Science
  • Eng. Appl. Artif. Intell.
  • 2018
A unified framework based on a deep convolutional framework is proposed to detect abnormal human behavior from a standard RGB image to improve detection speed while maintaining recognition accuracy. Expand