Abstract
Recently, with the increase of the car ownership, transportation network becomes more and more complex, the traffic management system is facing a serious challenge. Therefore, vehicle recognition plays a decisive role. The vehicle logo contains information of a vehicle, so vehicle logo can be used to identify vehicle models. Considering the problem of the high calculation cost of the vehicle recognition based on sparse representation, a vehicle logo recognition method based on optimized dictionary and robust collaborative representation is proposed. This method not only has a high recognition rate but also reduces the computation time. In our method, HOG feature is selected to represent the vehicle logo image. HOG features are extracted from training samples and testing samples respectively, and then dictionary learning method based on Fisher discrimination criterion is adopted to optimize the train samples. Finally, robust collaborative representation is used to recognize the vehicle models. The experimental results show that our method achieves a high recognition rate 98.2% and reduces the computation time.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: DEStech Transactions on Engineering and Technology Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
