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Computer vision for traffic surveillance systems : Methods and applications

By: Language: English Series: Blekinge Institute of Technology Doctoral Dissertation Series ; 2021:01 | Blekinge Institute of Technology Doctoral Dissertation Series ; 2021:01Publication details: Karlskrona : Blekinge tekniska högskola, 2021Description: 172 sISBN:
  • 9789172954168
Subject(s): Online resources: Notes: Härtill 8 uppsatserTeknologie doktorsexamen Dissertation note: Diss. (sammanfattning), Karlskrona : Blekinge tekniska högskola, 2021 Abstract: Computer vision solutions play a significant role in intelligent transportation systems (ITS) by improving traffic flow, safety and management. In addition, they feature prominently in autonomous vehicles and their future development. The main advantages of vision-based systems are their flexibility, coverage and accessibility. Moreover, computational power and recent algorithmic advances have increased the promise of computer vision solutions and broadened their implementation. However, computational complexity, reliability and efficiency remain among the challenges facing vision-based systems. Most traffic surveillance systems in ITS comprise three major criteria: vehicle detection, tracking and classification. In this thesis, computer vision systems are introduced to accomplish goals corresponding to these three criteria: 1) to detect the changed regions of an industrial harbour's parking lot using aerial images, 2) to estimate the speed of the vehicles on the road using a stationary roadside camera and 3) to classify vehicles using a stationary roadside camera and aerial images. The first part of this thesis discusses change detection in aerial images, which is the core of many remote sensing applications. The second part of this thesis deals with vehicle speed estimation using roadside cameras. Finally, in the third part, two vehicle classification models are proposed for roadside and aerial images.
Item type: Dissertation
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Härtill 8 uppsatser

Teknologie doktorsexamen

Diss. (sammanfattning), Karlskrona : Blekinge tekniska högskola, 2021

Computer vision solutions play a significant role in intelligent transportation systems (ITS) by improving traffic flow, safety and management. In addition, they feature prominently in autonomous vehicles and their future development. The main advantages of vision-based systems are their flexibility, coverage and accessibility. Moreover, computational power and recent algorithmic advances have increased the promise of computer vision solutions and broadened their implementation. However, computational complexity, reliability and efficiency remain among the challenges facing vision-based systems.

Most traffic surveillance systems in ITS comprise three major criteria: vehicle detection, tracking and classification. In this thesis, computer vision systems are introduced to accomplish goals corresponding to these three criteria: 1) to detect the changed regions of an industrial harbour's parking lot using aerial images, 2) to estimate the speed of the vehicles on the road using a stationary roadside camera and 3) to classify vehicles using a stationary roadside camera and aerial images. The first part of this thesis discusses change detection in aerial images, which is the core of many remote sensing applications. The second part of this thesis deals with vehicle speed estimation using roadside cameras. Finally, in the third part, two vehicle classification models are proposed for roadside and aerial images.