Yuanwei Wu

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: This study reveals that deep features provide prominent semantic information and a variety of contextual contents, which contribute to its superior performance in detecting small or occluded objects.

TL;DR: In this article, a real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model, where the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame.

TL;DR: A fast and robust obstacle detection and tracking approach by integrating an adaptive obstacle detection strategy within a kernelized correlation filter (KCF) framework in this paper, which significantly outperforms the state-of theart methods in terms of tracking speed and accuracy.

TL;DR: In this chapter, a brief overview of the recent development in object detection using convolutional neural networks (CNN) is presented and several classical CNN-based detectors are presented.