Camera-based Vehicle Recognition Methods and Techniques: Systematic Literature Review
Intelligent Transportation System (ITS) has been explored and widely used in modern cities. Most ITS studies aim to investigate novel vehicle system approaches which are vehicle detection, tracking or recognition. The vehicle recognition system is one of the technology applications widely used in IT...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
American Scientific Publisher
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/19974/ http://umpir.ump.edu.my/id/eprint/19974/ http://umpir.ump.edu.my/id/eprint/19974/ http://umpir.ump.edu.my/id/eprint/19974/1/Camera-based%20Vehicle%20Recognition%20Methods%20and%20Techniques.pdf |
| Summary: | Intelligent Transportation System (ITS) has been explored and widely used in modern cities. Most ITS studies aim to investigate novel vehicle system approaches which are vehicle detection, tracking or recognition. The vehicle recognition system is one of the technology applications widely used in ITS. It is implemented to recognize vehicles using camera-based. However, there are several issues regarding these approaches such as unable to provide reliable results and performance, especially in the complex environment such as lighting, shadow, and occlusion. This paper aims to present a systematic literature review of vehicle recognition by presenting state of the art, methods and the processes. The vehicle recognition categorized into two types of approaches which are appearance and motion based. The appearance-based obtained the data from the static image while motion-based used the data from video. To extract the data, the standard processes used are pre-processing, feature extraction, feature selection, and classification. Based on existing works, the majority focused on a vehicle type recognition system, to offer an alternative to current practice in many related systems such as automatic toll system, traffic monitoring, vehicle counting, traffic census and others that used human observation or sensor-based on classifying the vehicle type. To conclude, this paper discussed the related findings and used for further research in overcoming challenges and issues in the vehicle recognition especially in the vehicle type recognition. |
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