Employing Topical Relations in Semantic Analysis of Traffic Videos

Motion patterns in traffic video can be directly exploited to generate high-level descriptions of video content, which can be used for rule mining and abnormal event detection. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this p...

Full description

Saved in:
Bibliographic Details
Published in:IEEE intelligent systems 2019-01, Vol.34 (1), p.3-13
Main Authors: Ahmadi, Parvin, Gholampour, Iman, Tabandeh, Mahmoud
Format: Article
Language:English
Subjects:
Analytical models
artificial intelligence
computing methodologies
Event detection
Image analysis
image processing and computer vision
Intelligent systems
Intelligent vehicles
Motion control
Object recognition
Scene analysis
Semantic analysis
Semantics
Traffic control
Videos
vision and scene understanding
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Motion patterns in traffic video can be directly exploited to generate high-level descriptions of video content, which can be used for rule mining and abnormal event detection. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this paper, a topic related sparse topical coding framework is proposed for more effectively discovering motion patterns in traffic videos.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2018.111144040