scholarly journals Human Action Recognition Using Recurrent Bag-of-Features Pooling

2021 ◽  
pp. 63-76
Author(s):  
Marios Krestenitis ◽  
Nikolaos Passalis ◽  
Alexandros Iosifidis ◽  
Moncef Gabbouj ◽  
Anastasios Tefas
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Bag Of Features

Local Spatiotemporal Coding and Sparse Representation Based Human Action Recognition

2013 ◽  
Vol 401-403 ◽  
pp. 1555-1560
Author(s):  
Bin Wang ◽  
Yu Liu ◽  
Wei Wang ◽  
Wei Xu ◽  
Mao Jun Zhang
Keyword(s):  
Sparse Representation ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Local Features ◽  
Space Time ◽  
Video Classification ◽  
Bag Of Features ◽  
Temporal Relationships ◽  
Multi Level

To handle with the limitation of bag-of-features (BoF) model which ignores spatial and temporal relationships of local features in human action recognition in video, a Local Spatiotemporal Coding (LSC) is proposed. Rather than the exiting methods only uses the feature appearance information for coding, LSC encodes feature appearance and spatiotemporal positions information simultaneously with vector quantization (VQ). It can directly models the spatiotemporal relationships of local features in space time volume (STV). In implement, the local features are projected into sub-space-time-volume (sub-STV), and encoded with LSC. In addition a multi-level LSC is also provided. Then a group of sub-STV descriptors obtained from videos with multi-level LSC and Avg-pooling are used for action video classification. A sparse representation based classification method is adopted to classify action videos upon these sub-STV descriptors. The experimental results on KTH, Weizmann, and UCF sports datasets show that our method achieves better performance than the previous local spatiotemporal features based human action recognition methods.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

Human Action Recognition Based on Discriminative Sparse Coding Video Representation

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 745 ◽  
Author(s):  
Bin WANG ◽  
Yuanyuan WANG ◽  
Wenhua XIAO ◽  
Wei WANG ◽  
Maojun ZHANG
Keyword(s):  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Representation

Fusion of Skeleton and RGB Features for RGB-D Human Action Recognition

2021 ◽  
pp. 1-1
Author(s):  
Xu Weiyao ◽  
Wu Muqing ◽  
Zhao Min ◽  
Xia Ting
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action
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