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Triple-feature-based Particle Filter Algorithm Used in Vehicle Tracking Applications
- Source :
- Advances in Electrical and Computer Engineering, Vol 21, Iss 2, Pp 3-14 (2021)
- Publication Year :
- 2021
- Publisher :
- Stefan cel Mare University of Suceava, 2021.
-
Abstract
- This work is oriented toward video tracking of vehicles in a typical traffic environment, based on particle filters. The proposed tracking algorithm is based on simultaneous usage of three different image features - color, edge orientation, and texture. All three features are related to the contents of a rectangular window that includes both the vehicle that is tracked and local background and they are represented in the form of appropriate histograms. Based on individual estimates produced by every single feature, the resultant estimate is made by their weighted averaged. Weighting factors are adaptively changing depending on the quality of a particular feature, estimated by calculations of average similarities between the reference window and the set of windows on particles' positions. The tracking accuracies of single-feature and three-features-based filters have been verified using the set of traffic sequences illustrating the presence of typical disturbances (shadows, partial and full occlusions, maneuvering etc.).
- Subjects :
- Computer engineering. Computer hardware
General Computer Science
business.industry
Computer science
image color analysis
particle filters
image edge detection
Tracking (particle physics)
TK1-9971
TK7885-7895
Video tracking
Feature based
In vehicle
image texture analysis
Computer vision
Artificial intelligence
Electrical engineering. Electronics. Nuclear engineering
Electrical and Electronic Engineering
image sequence analysis
business
Particle filter
Particle filtering algorithm
Subjects
Details
- Language :
- English
- ISSN :
- 18447600 and 15827445
- Volume :
- 21
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Advances in Electrical and Computer Engineering
- Accession number :
- edsair.doi.dedup.....7facf71b55cf1f761c8b80035262b901