  Multi-object tracking (MOT) is an important and practical task related to
both surveillance systems and moving camera applications, such as autonomous
driving and robotic vision. However, due to unreliable detection, occlusion and
fast camera motion, tracked targets can be easily lost, which makes MOT very
challenging. Most recent works treat tracking as a re-identification (Re-ID)
task, but how to combine appearance and temporal features is still not well
addressed. In this paper, we propose an innovative and effective tracking
method called TrackletNet Tracker (TNT) that combines temporal and appearance
information together as a unified framework. First, we define a graph model
which treats each tracklet as a vertex. The tracklets are generated by
appearance similarity with CNN features and intersection-over-union (IOU) with
epipolar constraints to compensate camera movement between adjacent frames.
Then, for every pair of two tracklets, the similarity is measured by our
designed multi-scale TrackletNet. Afterwards, the tracklets are clustered into
groups which represent individual object IDs. Our proposed TNT has the ability
to handle most of the challenges in MOT, and achieve promising results on MOT16
and MOT17 benchmark datasets compared with other state-of-the-art methods.
