Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers

2013 ◽  
Vol 27 (4) ◽  
pp. 652-663 ◽  
Author(s):  
Mani Golparvar-Fard ◽  
Arsalan Heydarian ◽  
Juan Carlos Niebles
Keyword(s):  
Support Vector Machine ◽  
Action Recognition ◽  
Support Vector ◽  
Temporal Features ◽  
Spatio Temporal ◽  
Earthmoving Equipment

Action Recognition with Spatiotemporal Analysis and Support Vector Machine for Elderly Monitoring System

2021 ◽  
Author(s):  
Mahaputra Ilham Awal ◽  
Luqmanul Hakim Iksan ◽  
Rizky Zull Fhamy ◽  
Dwi Kurnia Basuki ◽  
Sritrusta Sukaridhoto ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Monitoring System ◽  
Action Recognition ◽  
Spatiotemporal Analysis ◽  
Support Vector

scholarly journals Multi-Term Attention Networks for Skeleton-Based Action Recognition

2020 ◽  
Vol 10 (15) ◽  
pp. 5326
Author(s):  
Xiaolei Diao ◽  
Xiaoqiang Li ◽  
Chen Huang
Keyword(s):  
Neural Network ◽  
Time Scales ◽  
Action Recognition ◽  
State Of The Art ◽  
Attention Networks ◽  
Weighted Fusion ◽  
Temporal Features ◽  
Benchmark Datasets ◽  
Spatio Temporal ◽  
Different Time Scales

The same action takes different time in different cases. This difference will affect the accuracy of action recognition to a certain extent. We propose an end-to-end deep neural network called “Multi-Term Attention Networks” (MTANs), which solves the above problem by extracting temporal features with different time scales. The network consists of a Multi-Term Attention Recurrent Neural Network (MTA-RNN) and a Spatio-Temporal Convolutional Neural Network (ST-CNN). In MTA-RNN, a method for fusing multi-term temporal features are proposed to extract the temporal dependence of different time scales, and the weighted fusion temporal feature is recalibrated by the attention mechanism. Ablation research proves that this network has powerful spatio-temporal dynamic modeling capabilities for actions with different time scales. We perform extensive experiments on four challenging benchmark datasets, including the NTU RGB+D dataset, UT-Kinect dataset, Northwestern-UCLA dataset, and UWA3DII dataset. Our method achieves better results than the state-of-the-art benchmarks, which demonstrates the effectiveness of MTANs.

scholarly journals Action recognition based on histogram of oriented gradients and spatio-temporal interest points

2018 ◽  
Vol 7 (4) ◽  
pp. 2153
Author(s):  
P A. Dhulekar ◽  
S T. Gandhe
Keyword(s):  
Action Recognition ◽  
Support Vector ◽  
Camera Motion ◽  
Histogram Of Oriented Gradients ◽  
K Nearest Neighbor ◽  
Interest Points ◽  
Human Machine Interaction ◽  
Training Time ◽  
Wide Range ◽  
Spatio Temporal

In modern years large extent of the work has been carried out to recognize human actions perhaps because of its wide range of applications in the field of surveillance, human-machine interaction and video analysis. Several methods were proposed by researchers to resolve action recognition challenges such as variations in viewpoints, occlusion, cluttered backgrounds and camera motion. To address these challenges, we propose a novel method comprise of features extraction using histogram of oriented gradients (HOG), and their classification using k-nearest neighbor (k-NN) and support vector machine (SVM). Six different experimentations were carried out on the basis of hybrid combinations of feature extractors and classifiers. Two gold standard datasets; KTH and Weizmann were used for training and testing purpose. The quantitative parameters such as recognition accuracy, training time and prediction speed were used for evaluation. To validate the applicability of proposed algorithm, its performance has been compared with spatio-temporal interest points (STIP) technique which was proposed as state of art method in the domain. 

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