The Vision Meets Drone (VisDrone2020) Single Object Tracking is the third annual UAV tracking evaluation activity organized by the VisDrone team, in conjunction with European Conference on Computer Vision (ECCV 2020). The VisDrone-SOT2020 Challenge presents and discusses the results of 13 participating algorithms in detail. By using ensemble of different trackers trained on several large-scale datasets, the top performer in VisDrone-SOT2020 achieves better results than the counterparts in VisDrone-SOT2018 and VisDrone-SOT2019. The challenging results, collected videos as well as the valuation toolkit are made available at http://aiskyeye.com/ . By holding VisDrone-SOT2020 challenge, we hope to provide the community a dedicated platform for developing and evaluating drone-based tracking approaches.

Effectively keeping boundary of the mask complete is important in instance segmentation. In this task, many works segment instance based on a bounding box from the box head, which means the quality of the detection also affects the completeness of the mask. To circumvent this issue, we propose a fully convolutional box head and a supervised edge attention module in mask head. The box head contains one new IoU prediction branch. It learns association between object features and detected bounding boxes to provide more accurate bounding boxes for segmentation. The edge attention module utilizes attention mechanism to highlight object and suppress background noise, and a supervised branch is devised to guide the network to focus on the edge of instances precisely. To evaluate the effectiveness, we conduct experiments on COCO dataset. Without bells and whistles, our approach achieves impressive and robust improvement compared to baseline models. Code is at https://github.com//IPIU-detection/SEANet.

Pose tracking is a crucial issue for many applications such as robotic tasks and facility operations. Vision-based approaches with non-contact properties are appropriate choices for these tasks. However, vision-based approaches are not sufficiently robust and fast. In this work, we propose a vision-based pose tracking to deal with these problems. We estimate poses using Lie group and Lie algebra representation theory. Such operation is performed in a linearized space, therefore it is convenient for pose estimation. To provide reliable visual information for our pose estimation, we detect line segments. Our detection of line segment depends on semi-global image information. We describe all line segments and match those detected in consecutive frames. Our line segment detector and matching descriptor are good at discarding ambiguous line segments and finding real ones in noisy situations. The integration of group theory and line segment detection and matching plays an important role for developing a robust vision-based pose tracking system. Our system proves to be efficient and robust.

The Vision Meets Drone (VisDrone2019) Single Object Tracking challenge is the second annual research activity focusing on evaluating single-object tracking algorithms on drones, held in conjunction with the International Conference on Computer Vision (ICCV 2019). The VisDrone-SOT2019 Challenge goes beyond its VisDrone-SOT2018 predecessor by introducing 25 more challenging sequences for long-term tracking. We evaluate and discuss the results of 22 participating algorithms and 19 state-of-the-art trackers on the collected dataset. The challenge results are publicly available at the website: http://www.aiskyeye.com/. WeexpecttheVisDrone-SOTchallenge to boost the research in single object tracking field.