  Video object detection targets to simultaneously localize the bounding boxes
of the objects and identify their classes in a given video. One challenge for
video object detection is to consistently detect all objects across the whole
video. As the appearance of objects may deteriorate in some frames, features or
detections from the other frames are commonly used to enhance the prediction.
In this paper, we propose a Plug & Play scale-adaptive convolutional regression
tracker for the video object detection task, which could be easily and
compatibly implanted into the current state-of-the-art detection networks. As
the tracker reuses the features from the detector, it is a very light-weighted
increment to the detection network. The whole network performs at the speed
close to a standard object detector. With our new video object detection
pipeline design, image object detectors can be easily turned into efficient
video object detectors without modifying any parameters. The performance is
evaluated on the large-scale ImageNet VID dataset. Our Plug & Play design
improves mAP score for the image detector by around 5% with only little speed
drop.

Computer Vision and Pattern Recognition