Authors
Pengfei Zhu, Longyin Wen, Dawei Du, Xiao Bian, Haibin Ling, Qinghua Hu, Qinqin Nie, Hao Cheng, Chenfeng Liu, Xiaoyu Liu, Wenya Ma, Haotian Wu, Lianjie Wang, Arne Schumann, Chase Brown, Chen Qian, Chengzheng Li, Dongdong Li, Emmanouil Michail, Fan Zhang, Feng Ni, Feng Zhu, Guanghui Wang, Haipeng Zhang, Han Deng, Hao Liu, Haoran Wang, Heqian Qiu, Honggang Qi, Honghui Shi, Hongliang Li, Hongyu Xu, Hu Lin, Ioannis Kompatsiaris, Jian Cheng, Jianqiang Wang, Jianxiu Yang, Jingkai Zhou, Juanping Zhao, KJ Joseph, Kaiwen Duan, Karthik Suresh, Bo Ke, Ke Wang, Konstantinos Avgerinakis, Lars Sommer, Lei Zhang, Li Yang, Lin Cheng, Lin Ma, Liyu Lu, Lu Ding, Minyu Huang, Naveen Kumar Vedurupaka, Nehal Mamgain, Nitin Bansal, Oliver Acatay, Panagiotis Giannakeris, Qian Wang, Qijie Zhao, Qingming Huang, Qiong Liu, Qishang Cheng, Qiuchen Sun, Robert LaganiŔre, Sheng Jiang, Shengjin Wang, Shubo Wei, Siwei Wang, Stefanos Vrochidis, Wang
Publication date
2018/9/9
Conference
European Conference on Computer Vision (ECCV) Workshops
Publisher
Springer, Cham
Description
Object detection is a hot topic with various applications in computer vision, eg, image understanding, autonomous driving, and video surveillance. Much of the progresses have been driven by the availability of object detection benchmark datasets, including PASCAL VOC, ImageNet, and MS COCO. However, object detection on the drone platform is still a challenging task, due to various factors such as view point change, occlusion, and scales. To narrow the gap between current object detection performance and the real-world requirements, we organized the Vision Meets Drone (VisDrone2018) Object Detection in Image challenge in conjunction with the 15th European Conference on Computer Vision (ECCV 2018). Specifically, we release a large-scale drone-based dataset, including 8,599 images (6,471 for training, 548 for validation, and 1,580 for testing) with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc. Featuring a diverse real-world scenarios, the dataset was collected using various drone models, in different scenarios (across 14 different cities spanned over thousands of kilometres), and under various weather and lighting conditions. We mainly focus on ten object categories in object detection, ie, pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle. Some rarely occurring special vehicles (eg, machineshop truck, forklift truck, and tanker) are ignored in evaluation. The dataset is extremely challenging due to various factors, including large scale and pose variations, occlusion, and clutter background. We present the evaluation protocol of the VisDrone …
Scholar articles
P Zhu, L Wen, D Du, X Bian, H Ling, Q Hu, Q Nie… - Proceedings of the European Conference on Computer …, 2018