Human action recognition using topic model

2014 ◽  
Author(s):  
Qian Xiao ◽  
Jun Cheng
Keyword(s):  
Action Recognition ◽  
Topic Model ◽  
Human Action Recognition ◽  
Human Action

scholarly journals Adaptive Self-Occlusion Behavior Recognition Based on pLSA

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hong-bin Tu ◽  
Li-min Xia ◽  
Lun-zheng Tan
Keyword(s):  
Random Field ◽  
Action Recognition ◽  
Markov Random Field ◽  
Semantic Analysis ◽  
Topic Model ◽  
Human Action Recognition ◽  
Human Action ◽  
Probabilistic Latent Semantic Analysis ◽  
Behavior Recognition ◽  
Markov Random

Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize the human behavior. Experiments were performed on the KTH, Weizmann, and Humaneva dataset to test and evaluate the proposed method. The compared experiment results showed that what the proposed method can achieve was more effective than the compared methods.

Discriminative Action Recognition Using Supervised Latent Topic Model

2012 ◽  
Vol 190-191 ◽  
pp. 1125-1128
Author(s):  
Huan Xin Zou ◽  
Hao Sun ◽  
Ke Feng Ji
Keyword(s):  
Action Recognition ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Sequences ◽  
Discriminative Learning ◽  
Action Categorization ◽  
Latent Topic ◽  
Topic Structure

We present a discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure which is most relevant to action categorization. We test our algorithm on two challenging datasets. Experimental results demonstrate the effectiveness of our algorithm.

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

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.

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