scholarly journals Group Sparse Regression-Based Learning Model for Real-Time Depth-Based Human Action Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Meng Li ◽  
Liang Yan ◽  
Qianying Wang
Keyword(s):  
Real Time ◽  
Human Action Recognition ◽  
Human Action ◽  
Group Sparsity ◽  
Human Actions ◽  
Depth Data ◽  
Human Object ◽  
Benchmark Datasets ◽  
Object Interactions ◽  
Sparse Set

This paper addresses the problem of predicting human actions in depth videos. Due to the complex spatiotemporal structure of human actions, it is difficult to infer ongoing human actions before they are fully executed. To handle this challenging issue, we first propose two new depth-based features called pairwise relative joint orientations (PRJOs) and depth patch motion maps (DPMMs) to represent the relative movements between each pair of joints and human-object interactions, respectively. The two proposed depth-based features are suitable for recognizing and predicting human actions in real-time fashion. Then, we propose a regression-based learning approach with a group sparsity inducing regularizer to learn action predictor based on the combination of PRJOs and DPMMs for a sparse set of joints. Experimental results on benchmark datasets have demonstrated that our proposed approach significantly outperforms existing methods for real-time human action recognition and prediction from depth data.

scholarly journals Complex Human–Object Interactions Analyzer Using a DCNN and SVM Hybrid Approach

2019 ◽  
Vol 9 (9) ◽  
pp. 1869 ◽  
Author(s):  
Cho Nilar Phyo ◽  
Thi Thi Zin ◽  
Pyke Tin
Keyword(s):  
Hybrid Approach ◽  
Human Action Recognition ◽  
Cost Effective ◽  
The Elderly ◽  
Human Action ◽  
Daily Activities ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Human Object ◽  
Object Interactions

Nowadays, with the emergence of sophisticated electronic devices, human daily activities are becoming more and more complex. On the other hand, research has begun on the use of reliable, cost-effective sensors, patient monitoring systems, and other systems that make daily life more comfortable for the elderly. Moreover, in the field of computer vision, human action recognition (HAR) has drawn much attention as a subject of research because of its potential for numerous cost-effective applications. Although much research has investigated the use of HAR, most has dealt with simple basic actions in a simplified environment; not much work has been done in more complex, real-world environments. Therefore, a need exists for a system that can recognize complex daily activities in a variety of realistic environments. In this paper, we propose a system for recognizing such activities, in which humans interact with various objects, taking into consideration object-oriented activity information, the use of deep convolutional neural networks, and a multi-class support vector machine (multi-class SVM). The experiments are performed on a publicly available cornell activity dataset: CAD-120 which is a dataset of human–object interactions featuring ten high-level daily activities. The outcome results show that the proposed system achieves an accuracy of 93.33%, which is higher than other state-of-the-art methods, and has great potential for applications recognizing complex daily activities.

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

scholarly journals Exploring 3D Human Action Recognition Using STACOG on Multi-View Depth Motion Maps Sequences

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3642
Author(s):  
Mohammad Farhad Bulbul ◽  
Sadiya Tabussum ◽  
Hazrat Ali ◽  
Wenli Zheng ◽  
Mi Young Lee ◽  
...  
Keyword(s):  
Action Recognition ◽  
Depth Map ◽  
Human Action Recognition ◽  
Human Action ◽  
Collaborative Representation ◽  
Auto Correlation ◽  
Time Operation ◽  
Real Time Operation ◽  
Benchmark Datasets ◽  
Depth Motion Maps

This paper proposes an action recognition framework for depth map sequences using the 3D Space-Time Auto-Correlation of Gradients (STACOG) algorithm. First, each depth map sequence is split into two sets of sub-sequences of two different frame lengths individually. Second, a number of Depth Motion Maps (DMMs) sequences from every set are generated and are fed into STACOG to find an auto-correlation feature vector. For two distinct sets of sub-sequences, two auto-correlation feature vectors are obtained and applied gradually to L2-regularized Collaborative Representation Classifier (L2-CRC) for computing a pair of sets of residual values. Next, the Logarithmic Opinion Pool (LOGP) rule is used to combine the two different outcomes of L2-CRC and to allocate an action label of the depth map sequence. Finally, our proposed framework is evaluated on three benchmark datasets named MSR-action 3D dataset, DHA dataset, and UTD-MHAD dataset. We compare the experimental results of our proposed framework with state-of-the-art approaches to prove the effectiveness of the proposed framework. The computational efficiency of the framework is also analyzed for all the datasets to check whether it is suitable for real-time operation or not.

scholarly journals 2D Information Space Based Action Recognition

2019 ◽  
Vol 9 (1S) ◽  
pp. 295-298
Keyword(s):  
Convex Hull ◽  
Action Recognition ◽  
Video Sequence ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Human Action ◽  
New Approach ◽  
Human Object ◽  
Background Data ◽  
2D Data

Video based human action recognition has attained more attraction from the researchers and it predominates in the field of computer vision and pattern recognition. In this paper we deliver a new approach to suppress the background data and to extract 2D data of foreground human object of the video sequence. A combination of convex hull area, convex hull perimeter, solidity and eccentricity is used to represent the feature vector. Experiments are conducted on Weizmann video dataset to assess how the system is doing. The discriminative nature of the feature vectors assures accuracy in action recognition.

scholarly journals A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Alexandros Andre Chaaraoui ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Assisted Living ◽  
Learning Algorithm ◽  
Ambient Assisted Living ◽  
Human Action Recognition ◽  
Human Action ◽  
Sequence Matching ◽  
Low Dimensional ◽  
Video Frequency

This paper presents a novel silhouette-based feature for vision-based human action recognition, which relies on the contour of the silhouette and a radial scheme. Its low-dimensionality and ease of extraction result in an outstanding proficiency for real-time scenarios. This feature is used in a learning algorithm that by means of model fusion of multiple camera streams builds a bag of key poses, which serves as a dictionary of known poses and allows converting the training sequences into sequences of key poses. These are used in order to perform action recognition by means of a sequence matching algorithm. Experimentation on three different datasets returns high and stable recognition rates. To the best of our knowledge, this paper presents the highest results so far on the MuHAVi-MAS dataset. Real-time suitability is given, since the method easily performs above video frequency. Therefore, the related requirements that applications as ambient-assisted living services impose are successfully fulfilled.

Smart Surveillance and Real-Time Human Action Recognition Using OpenPose

2020 ◽  
pp. 504-509
Author(s):  
Vatsal Rathod ◽  
Rishvanth Katragadda ◽  
Saurabh Ghanekar ◽  
Saurav Raj ◽  
Pushyamitra Kollipara ◽  
...  
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Smart Surveillance
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