  Object detection in video is crucial for many applications. Compared to
images, video provides additional cues which can help to disambiguate the
detection problem. Our goal in this paper is to learn discriminative models for
the temporal evolution of object appearance and to use such models for object
detection. To model temporal evolution, we introduce space-time tubes
corresponding to temporal sequences of bounding boxes. We propose two CNN
architectures for generating and classifying tubes, respectively. Our tube
proposal network (TPN) first generates a large number of spatio-temporal tube
proposals maximizing object recall. The Tube-CNN then implements a tube-level
object detector in the video. Our method improves state of the art on two
large-scale datasets for object detection in video: HollywoodHeads and ImageNet
VID. Tube models show particular advantages in difficult dynamic scenes.
