scholarly journals Exploiting better motion cues for better action recognition

2021 ◽  
Author(s):  
Jawad Khan
Keyword(s):  
Action Recognition ◽  
Space Time ◽  
Motion Cues ◽  
Large Margin ◽  
Additional Information ◽  
Recognition Algorithms ◽  
Video Description ◽  
Shear Characteristics

Several recent studies on action recognition have emphasised the significance of including motioncharacteristics clearly in the video description. This work shows that properly partitioning visualmotion into dominant and residual motions enhances action recognition algorithms greatly, both interms of extracting space-time trajectories and computing descriptors. Then, using differentialmotion scalar variables, divergence, curl, and shear characteristics, we create a new motiondescriptor, the DCS descriptor. It adds to the results by capturing additional information on localmotion patterns. Finally, adopting the recently proposed VLAD coding technique in image retrievalimproves action recognition significantly. On three difficult datasets, namely Hollywood 2,HMDB51, and Olympic Sports, our three additions are complementary and lead to beat all reportedresults by a large margin.

scholarly journals Space-Time Robust Representation for Action Recognition

2013 ◽  
Author(s):  
Nicolas Ballas ◽  
Yi Yang ◽  
Zhen-Zhong Lan ◽  
Bertrand Delezoide ◽  
Francoise Preteux ◽  
...  
Keyword(s):  
Action Recognition ◽  
Space Time ◽  
Robust Representation

scholarly journals Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1447
Author(s):  
Pan Huang ◽  
Yanping Li ◽  
Xiaoyi Lv ◽  
Wen Chen ◽  
Shuxian Liu
Keyword(s):  
Feature Selection ◽  
Action Recognition ◽  
Nearest Neighbor ◽  
Health Indicators ◽  
Support Vector ◽  
Normal Walking ◽  
K Nearest Neighbor ◽  
Recognition Algorithms ◽  
Medical Health ◽  
Improved Algorithm

Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF.

Unsafe Action Recognition of Miners Based on Video Description

2019 ◽  
Author(s):  
Enjie Ding ◽  
Zhongyu Liu ◽  
Yafeng Liu ◽  
Dawei Xu ◽  
Shimin Feng ◽  
...  
Keyword(s):  
Action Recognition ◽  
Video Description

scholarly journals A Hybrid Network for Large-Scale Action Recognition from RGB and Depth Modalities

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3305 ◽  
Author(s):  
Huogen Wang ◽  
Zhanjie Song ◽  
Wanqing Li ◽  
Pichao Wang
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Canonical Correlation ◽  
Large Scale ◽  
State Of The Art ◽  
Hybrid Network ◽  
Support Vector ◽  
Multiple Modalities ◽  
Large Margin ◽  
Percentage Points

The paper presents a novel hybrid network for large-scale action recognition from multiple modalities. The network is built upon the proposed weighted dynamic images. It effectively leverages the strengths of the emerging Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches to specifically address the challenges that occur in large-scale action recognition and are not fully dealt with by the state-of-the-art methods. Specifically, the proposed hybrid network consists of a CNN based component and an RNN based component. Features extracted by the two components are fused through canonical correlation analysis and then fed to a linear Support Vector Machine (SVM) for classification. The proposed network achieved state-of-the-art results on the ChaLearn LAP IsoGD, NTU RGB+D and Multi-modal & Multi-view & Interactive ( M 2 I ) datasets and outperformed existing methods by a large margin (over 10 percentage points in some cases).

On the improvement of human action recognition from depth map sequences using Space–Time Occupancy Patterns

2014 ◽  
Vol 36 ◽  
pp. 221-227 ◽  
Author(s):  
Antonio W. Vieira ◽  
Erickson R. Nascimento ◽  
Gabriel L. Oliveira ◽  
Zicheng Liu ◽  
Mario F.M. Campos
Keyword(s):  
Action Recognition ◽  
Depth Map ◽  
Human Action Recognition ◽  
Human Action ◽  
Space Time ◽  
Occupancy Patterns
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