Abstract
Vehicle detection in aerial imagery has been instrumental in a wide range of applications. Lately, as a result of the robust feature representations, convolutional neural networks (CNN) based detection methods have achieved prodigious performance in computer vision. The diversity of dataset sources that relate to vehicle images is numerous, but it is not sufficient in some cases due to the different types of vehicles from one country to another. In this paper, we propose a new dataset of vehicle images called AL-YOUSOFI taken in the Hashemite Kingdom of Jordan, Irbid city. The AL-YOUSOFI benchmark dataset consists of 40 challenging videos captured from real-world traffic scenes (over 158,000 frames with rich annotations, including vehicle type and vehicle bounding boxes) for multi-object detection and tracking. The state-of-the-art algorithms that have been previously trained on AL-YOUSOFI, has been evaluated. The results showed a variation in efficiency between the algorithms, and this is due to how each works. The full dataset is available at this URL.
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: International Journal For Multidisciplinary 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.
