Paper Info
Title
Unsupervised detection of ruptures in spatial relationships in video sequences based on log-likelihood ratio.
Abstract
In this work, we propose a new approach to automatically detect ruptures in spatial relationships in video sequences, based on low-level primitives, in unsupervised manner. The spatial relationships between two objects of interest are modeled using angle and distance histograms as examples. The evolution of the spatial relationships during time is estimated from the distances between two successive angle or distance histograms and then considered as a temporal signal. The evolution of a spatial relationship is modeled by a linear Gaussian model. Then, two hypotheses “without change” and “with change” are considered, and a log-likelihood ratio is computed. The distribution of the log-likelihood ratio, given that \(H_0\) is true, is estimated and used to compute the p value. The comparison of this p value to a significance level \(\alpha \), expressing the probability of false alarms, allows us to detect significant ruptures in spatial relationships during time. In addition, this approach is generalized to detect multiple object events such as merging, splitting, and other events that contain ruptures in their spatial relationships evolution. This work shows that the description of spatial relationships across time is a promising step toward event detection.
Year
DOI
Venue
2018
10.1007/s10044-017-0669-9
Pattern Anal. Appl.
Keywords
Field
DocType
Spatial relationships,Distances between histograms,Detection of ruptures,Hypotheses testing,Log-likelihood ratio,Significance level
Histogram,Pattern recognition,Likelihood-ratio test,p-value,Spatial relationship,Artificial intelligence,Gaussian network model,Merge (version control),Statistical hypothesis testing,Mathematics
Journal
Volume
Issue
ISSN
21
3
1433-7541
Citations 
PageRank 
References 
0
0.34
27
Authors
3
Name
Order
Citations
PageRank
Abdalbassir Abou-Elailah1383.69
Isabelle Bloch251.12
Valérie Gouet-brunet3699.90