Convolutional Neural Network for Trajectory Prediction
Computer Vision – ECCV 2018 Workshops
European Conference on Computer Vision
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Predicting trajectories of pedestrians is quintessential for autonomous robots which share the same environment with humans. In order to effectively and safely interact with humans, trajectory prediction needs to be both precise and computationally efficient. In this work, we propose a convolutional neural network (CNN) based human trajectory prediction approach. Unlike more recent LSTM-based moles which attend sequentially to each frame, our model supports increased parallelism and effective temporal representation. The proposed compact CNN model is faster than the current approaches yet still yields competitive results.
Convolutional neural network; Trajectory prediction; Anticipating human behavior
Artificial Intelligence and Robotics | Computer Engineering
Morris, B. T.
Convolutional Neural Network for Trajectory Prediction.
Computer Vision – ECCV 2018 Workshops, 11131
Munich, Germany: European Conference on Computer Vision.