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Current methods for trajectory prediction operate in supervised manners, andtherefore require vast quantities of corresponding ground truth data fortraining. In this paper, we present a novel, label-free algorithm,AutoTrajectory, for trajectory extraction and prediction to use raw videosdirectly. To better capture the moving objects in videos, we introduce dynamicpoints. We use them to model dynamic motions by using a forward-backwardextractor to keep temporal consistency and using image reconstruction to keepspatial consistency in an unsupervised manner. Then we aggregate dynamic pointsto instance points, which stand for moving objects such as pedestrians invideos. Finally, we extract trajectories by matching instance points forprediction training. To the best of our knowledge, our method is the first toachieve unsupervised learning of trajectory extraction and prediction. Weevaluate the performance on well-known trajectory datasets and show that ourmethod is effective for real-world videos and can use raw videos to furtherimprove the performance of existing models.

AutoTrajectory: Label-free Trajectory Extraction and Prediction from Videos using Dynamic Points