  Recent online Multi-Object Tracking (MOT) methods have achieved desirable
tracking performance. However, the tracking speed of most existing methods is
rather slow. Inspired from the fact that the adjacent frames are highly
relevant and redundant, we divide the frames into key and non-key frames
respectively and track objects in the compressed domain. For the key frames,
the RGB images are restored for detection and data association. To make data
association more reliable, an appearance Convolutional Neural Network (CNN)
which can be jointly trained with the detector is proposed. For the non-key
frames, the objects are directly propagated by a tracking CNN based on the
motion information provided in the compressed domain. Compared with the
state-of-the-art online MOT methods,our tracker is about 6x faster while
maintaining a comparable tracking performance.
