Indexed by:Journal paper
First Author:Tang,Peng
Correspondence Author:Wang,Wang,Jingdong,Liu,Wenyu
Co-author:Wang,Zeng,Wenjun,Xinggang,Wang,Chunyu
Journal:IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)
DOI number:10.1109/TPAMI.2019.2910529
Date of Publication:2019-04-11
Impact Factor:17.861
Abstract:Compared with object detection in static images, object detection in videos is more challenging due to degraded image qualities. An effective way to address this problem is to exploit temporal contexts by linking the same object across video to form tubelets and aggregating classification scores in the tubelets. In this paper, we focus on obtaining high quality object linking results for better classification. Unlike previous methods that link objects by checking boxes between neighboring frames, we propose to link in the same frame. To achieve this goal, we extend prior methods in following aspects: (1) a cuboid proposal network that extracts spatio-temporal candidate cuboids which bound the movement of objects; (2) a short tubelet detection network that detects short tubelets in short video segments; (3) a short tubelet linking algorithm that links temporally-overlapping short tubelets to form long tubelets. Experiments on the ImageNet VID dataset show that our method outperforms both the static image detector and the previous state of the art. In particular, our method improves results by 8.8 percent over the static image detector for fast moving objects.