  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% over the static image
detector for fast moving objects.
