Online data segmentation based on clustering algorithm and autoregressive model for human actions recognition

2017 ◽  
Author(s):  
M. Jiang ◽  
X.L. Liu ◽  
Z. Zhang ◽  
Y. Zhao ◽  
R. Zhang ◽  
...  
Keyword(s):  
Autoregressive Model ◽  
Clustering Algorithm ◽  
Online Data ◽  
Human Actions ◽  
Data Segmentation ◽  
Human Actions Recognition

scholarly journals Instantaneous Approach for Evaluating the Initial Centers in the Agricultural Databases Using K-Means Clustering Algorithm

2021 ◽  
Author(s):  
LNC. Prakash K ◽  
G. Surya Narayana ◽  
Mohd Dilshad Ansari ◽  
Vinit Kumar Gunjan
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Test Results ◽  
Data Segmentation ◽  
New Strategy ◽  
Time Required ◽  
Experimental Findings ◽  
The Right ◽  
The Given ◽  
Original Cluster

Clustering algorithms are most probably and widely used analysis method for grouping agricultural data with high similarity. For example, one of the most widely used approaches in previous study is K-means, which is simpler, more versatile, and easier to understand and formulate. The only disadvantage of the K-means algorithm has always been that the predetermined set of cluster centres must be prepared ahead of time and provided as feedback. This paper addresses the issue of estimating cluster random centres for data segmentation and proposes a new method for locating appropriate random centres based on the frequency of attribute values. As a consequence of calculating cluster random centres, the number of iterations required to achieve optimum clusters in K-means will be reduced, as will the time required to shape the final clusters. The experimental findings show that our approach is efficient at estimating the right random cluster centres that indicate a fair separation of objects in the given database. The technique observation and comparative test results showed that the new strategy does not use present manual cluster centres, is more efficient in determining the original cluster centres, and therefore more successful in terms of time to converge the actual clusters especially in agricultural data bases.

Research on Applied Technology in Human Action Recognition Based on Skeleton Information

2013 ◽  
Vol 859 ◽  
pp. 498-502 ◽  
Author(s):  
Zhi Qiang Wei ◽  
Ji An Wu ◽  
Xi Wang
Keyword(s):  
Data Analysis ◽  
Action Recognition ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Experimental Results ◽  
Human Actions ◽  
Applied Technology ◽  
Feature Sequence ◽  
Human Actions Recognition

In order to realize the identification of human daily actions, a method of identifying human daily actions is realized in this paper, which transforms this problem into converting human action recognition into analyzing feature sequence. Then the feature sequence combined with improved LCS algorithm could realize the human actions recognition. Data analysis and experimental results show the recognition rate of this method is high and speed is fast, and this applied technology will have broad prospects.

Human Action Recognition Based on Improved MIL

2015 ◽  
Vol 713-715 ◽  
pp. 2152-2155 ◽  
Author(s):  
Shao Ping Zhu
Keyword(s):  
Computer Vision ◽  
Action Recognition ◽  
Heuristic Algorithm ◽  
Human Action Recognition ◽  
Human Action ◽  
Image Sequences ◽  
Multiple Instance Learning ◽  
New Method ◽  
Human Actions ◽  
Human Actions Recognition

According to the problem that achieves robust human actions recognition from image sequences in computer vision, using the Iterative Querying Heuristic algorithm as a guide, a improved Multiple Instance Learning (MIL) method is proposed for human action recognition in video image sequences. Experiments show that the new method can quickly recognize human actions and achieve high recognition rates, and on the Weizmann database validate our analysis.

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