Al-yousofi: A Novel Benchmark For Multi-vehicle Detection And Tracking

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.