  Most of the existing tracking methods link the detected boxes to the
tracklets using a linear combination of feature cosine distances and box
overlap. But the problem of inconsistent features of an object in two different
frames still exists. In addition, when extracting features, only appearance
information is utilized, neither the location relationship nor the information
of the tracklets is considered. We present an accurate and end-to-end learning
framework for multi-object tracking, namely \textbf{TPAGT}. It re-extracts the
features of the tracklets in the current frame based on motion predicting,
which is the key to solve the problem of features inconsistent. The adaptive
graph neural network in TPAGT is adopted to fuse locations, appearance, and
historical information, and plays an important role in distinguishing different
objects. In the training phase, we propose the balanced MSE LOSS to
successfully overcome the unbalanced samples. Experiments show that our method
reaches state-of-the-art performance. It achieves 76.5\% MOTA on the MOT16
challenge and 76.2\% MOTA on the MOT17 challenge.
