Noticing Motion Patterns: Temporal CNN with a Novel Convolution Operator for Human Trajectory Prediction
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We propose a novel way to learn, detect and extract patterns in sequential data, and successfully applied it to the problem of human trajectory prediction. Our model, Social Pattern Extraction Convolution (Social-PEC), when compared to existing methods, achieves the best performance in terms of Average/Final Displacement Error. In addition, the proposed approach avoids the obscurity in the previous use of pooling layer, presenting intuitive and explainable decision making processes.
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