Temporal Unknown Incremental Clustering (TUIC) Model for Analysis of Traffic Surveillance Videos

Optimized scene representation is an important characteristic of a framework for detecting abnormalities on live videos. One of the challenges for detecting abnormalities in live videos is real-time detection of objects in a non-parametric way. Another challenge is to efficiently represent the state...

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Bibliographic Details
Published in:arXiv.org 2018-04
Main Authors: Kumaran, Santhosh Kelathodi, Dogra, Debi Prosad, Roy, Partha Pratim
Format: Article
Language:English
Subjects:
Abnormalities
Algorithms
Bayesian analysis
Clustering
Object recognition
Optical flow (image analysis)
Pixels
Real time
Traffic models
Traffic surveillance
Online Access:Get full text
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Summary:Optimized scene representation is an important characteristic of a framework for detecting abnormalities on live videos. One of the challenges for detecting abnormalities in live videos is real-time detection of objects in a non-parametric way. Another challenge is to efficiently represent the state of objects temporally across frames. In this paper, a Gibbs sampling based heuristic model referred to as Temporal Unknown Incremental Clustering (TUIC) has been proposed to cluster pixels with motion. Pixel motion is first detected using optical flow and a Bayesian algorithm has been applied to associate pixels belonging to similar cluster in subsequent frames. The algorithm is fast and produces accurate results in \(\Theta(kn)\) time, where \(k\) is the number of clusters and \(n\) the number of pixels. Our experimental validation with publicly available datasets reveals that the proposed framework has good potential to open-up new opportunities for real-time traffic analysis.
ISSN:2331-8422
DOI:10.48550/arxiv.1804.06680