Vehicle Detection Using Spatial Relationship GMM for Complex Urban Surveillance in Daytime and Nighttime

Traffic surveillance is an important issue in intelligent transportation systems. Efficient and accurate vehicle detection is one challenging problem for complex urban traffic surveillance. As such, this paper proposes a new vehicle detection method using spatial relationship GMM for daytime and nig...

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Vydáno v:International journal of parallel programming Ročník 46; číslo 5; s. 859 - 872
Hlavní autor: Song, Jun-fang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.10.2018
Springer Nature B.V
Témata:
Color
Computer Science
Daytime
Headlights
Intelligent transportation systems
Lamps
License plates
Localization
Night
Occlusion
Processor Architectures
Software Engineering/Programming and Operating Systems
Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm
Surveillance
Theory of Computation
Traffic surveillance
Vehicle identification
Part-based model
License plate detection
Vehicle detection
Gaussian mixture model
Rear lamp detection
Head light detection
ISSN:0885-7458, 1573-7640
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Shrnutí:Traffic surveillance is an important issue in intelligent transportation systems. Efficient and accurate vehicle detection is one challenging problem for complex urban traffic surveillance. As such, this paper proposes a new vehicle detection method using spatial relationship GMM for daytime and nighttime based on a high-resolution camera. First, the vehicle is treated as an object composed of multiple components, including the license plate, rear lamps and headlights. These components are localized using their distinctive color, texture, and region feature. Deriving plate color converting model, plate hypothesis score calculation and cascade plate refining were accomplished for license plate localization. Multi-threshold segmentation and connected component analysis are accomplished for rear lamps localization. Frame difference and geometric features similarity analysis are accomplished for headlights localization. After that, the detected components are taken to construct the spatial relationship using GMM. Finally, similar probability measures of the model and the GMM, including GMM of plate and rear lamp, GMM of both rear lamps and GMM of both headlights are adopted to localize vehicle. Experiments in practical urban scenarios are carried out under daytime and nighttime. It can be shown that our method can adapt to the partial occlusion and various lighting conditions well, meanwhile it has a fast detection speed.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-017-0543-9