Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction

Haddad, Sirin; Lam, Siew-Kei

doi:10.48550/arxiv.2012.06320

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We think this acceleration is an obvious advantage. As we can see in the figure, our best ADE achieved performance has a rise curve after some iterations, it is because we need to achieve both the FIGURE 5 The ADE test along with the convergence of the model. The proposed method surpassed the original model in earlier epochs by obvious advantages.…”

Section: Synergetic Trainingmentioning

confidence: 98%

“…Some work additionally take time sequences as the research subject [4], to explore the interactions among the pedestrians in time sequences. Previous works always intended to deploy handcrafted feature extraction encoders such as graph neural networks (GNN) [5], long shortterm memory (LSTM) [6], both LSTM and GNN [7], recurrent neural network (RNN) [8] and transformer model [1] to discriminate different state of pedestrian crowds. Most of these models ignored to figure out the reasonable route for training, and they assume the supervised learning is the proper training method or they seldomly consider the similarity measurement for training process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Contrastive learning of graph encoder for accelerating pedestrian trajectory prediction training

Yao

Ding

2021

IET Image Processing

View full text Add to dashboard Cite

In the area of pedestrian trajectory prediction, the hybrid structures of temporal feature extractor or spatial feature extractor have paved the way for the precise prediction model, and they are in larger and larger scale. Learning of specific feature encoding model not only influenced by the structure of the network, but also by the learning manners such as supervised learning and unsupervised learning. Previous works concentrated on more comprehensive encoders and more delicate designs of feature extractors. However, the mutual influence factors from the neighbour pedestrians associate with the distance to the centre pedestrian seldomly noticed. Most of the existed feature extractors in prediction models trained in the way of supervised learning other than unsupervised manners caused the problem that the extracted features are always handcrafted without the natural distinction of obscure situations. The graph contrastive accelerating encoder is proposed, which accelerates the pedestrian trajectory prediction training process of the state of the art method of spatio-temporal graph transformer networks. Employing the unsupervised contrastive learning process and the graph of neighbours representing distance affection of nearest and farthest pedestrian to the centre pedestrian, the graph contrastive accelerating encoder significantly shrinked the training time. Holding the final performance on to state of the art level, the proposed method let the lowest pedestrian trajectory prediction error show up in the obviously earlier training steps.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Synergetic Trainingmentioning

confidence: 98%