Convolutional Neural Network for Trajectory Prediction
arXiv:1809.00696
Abstract
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.
Accepted at ECCV 2018 workshop - Anticipating Human Behavior
References in corpus (6)
- Social Force Model for Pedestrian Dynamics
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- Convolutional Sequence to Sequence Learning
- Conditional Image Generation with PixelCNN Decoders
- DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
- Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection