  Deep Convolution Neural Networks (CNNs) have shown impressive performance in
various vision tasks such as image classification, object detection and
semantic segmentation. For object detection, particularly in still images, the
performance has been significantly increased last year thanks to powerful deep
networks (e.g. GoogleNet) and detection frameworks (e.g. Regions with CNN
features (R-CNN)). The lately introduced ImageNet task on object detection from
video (VID) brings the object detection task into the video domain, in which
objects' locations at each frame are required to be annotated with bounding
boxes. In this work, we introduce a complete framework for the VID task based
on still-image object detection and general object tracking. Their relations
and contributions in the VID task are thoroughly studied and evaluated. In
addition, a temporal convolution network is proposed to incorporate temporal
information to regularize the detection results and shows its effectiveness for
the task.
