  Multiple object tracking is to give each object an id in the video. The
difficulty is how to match the predicted objects and detected objects in same
frames. Matching features include appearance features, location features, etc.
These features of the predicted object are basically based on some previous
frames. However, few papers describe the relationship in the time domain
between the previous frame features and the current frame features.In this
paper, we proposed a time domain graph convolutional network for multiple
objects tracking.The model is mainly divided into two parts, we first use
convolutional neural network (CNN) to extract pedestrian appearance feature,
which is a normal operation processing image in deep learning, then we use GCN
to model some past frames' appearance feature to get the prediction appearance
feature of the current frame. Due to this extension, we can get the pose
features of the current frame according to the relationship between some frames
in the past. Experimental evaluation shows that our extensions improve the MOTA
by 1.3 on the MOT16, achieving overall competitive performance at high frame
rates.
