Kaiwen Duan

TL;DR: CenterNet as discussed by the authors detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall by enriching information collected by both the top-left and bottom-right corners and providing more recognizable information from the central regions.

TL;DR: This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs, and builds the framework upon a representative one-stage keypoint-based detector named CornerNet, which improves both precision and recall.

TL;DR: In this article, a new unconstrained UAV benchmark dataset is proposed for object detection, single object tracking, and multiple object tracking with new level challenges, including high density, small object, and camera motion, and a detailed quantitative study is performed using most recent state-of-the-art algorithms for each task.

TL;DR: A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.

TL;DR: In this article, a new unconstrained UAV benchmark is proposed for object detection, single object tracking, and multiple object tracking in complex scenarios with new level challenges, and the current state-of-the-art methods perform relative worse on the dataset, due to the new challenges appeared in UAV based real scenes, e.g., high density, small object, and camera motion.