Paper Info
Title
Multi-camera Background and Scene Activity Modelling Based on Spearman Correlation Analysis and Inception-V3 Network
Abstract
A novel approach for background and scene activity modelling with spearman correlation analysis and customized deep learning model is introduced in this paper. It detects and gives correlated analytics between casual and temporal regional activities on the basis of similarities and primary dissimilarities in the same scene captured by several cameras. The experiment implement on four overlapped videos that are captured inside the hall from four cameras. Detected and analyzed by our model, 17.32% correlated co-occurrences is actual correlation among all videos. Rest of 82.68% of videos is background that shows similar and repetitive features in spearman rank tied result. Simulation results demonstrate that the proposed method can detect high correlation among all activities during the frame rate with tied features ability.
Year
DOI
Venue
2019
10.1109/ICDEW.2019.00058
2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)
Keywords
Field
DocType
Video analysis, Spearman Correlation Analysis, Inception-V3, Cosine Distance Matrix
Data mining,Multi camera,Pattern recognition,Computer science,Correlation,Frame rate,Artificial intelligence,Deep learning,Analytics,Spearman's rank correlation coefficient
Conference
ISSN
ISBN
Citations 
1943-2895
978-1-7281-0891-9
0
PageRank 
References 
Authors
0.34
17
4
Name
Order
Citations
PageRank
Ke-Yang Cheng183.53
Muhammad Saddam Khokhar211.37
yunbo rao311.36
Rabia Tahir412.38