Unsupervised Pedestrian Trajectory Prediction with Graph Neural Networks

Wang, Mingkun; Shi, Dianxi; Guan, Naiyang; Zhang, Tao; Wang, Liujing; Li, Ruoxiang

doi:10.1109/ictai.2019.00119

Cited by 13 publications

(8 citation statements)

References 18 publications

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“…However, only the data cleansing with a vanilla LSTM attained an improvement for using a single trained network from a rate-4/5 decoder with the SRTR system, and vice versa. Similar works on an ablation study of clustering within LSTM have reported similar results on the vanilla LSTM's effectiveness [45]- [46].…”

Section: A Ablation Studies Related To Bpmr Systemssupporting

confidence: 67%

Long-Short Term Memory-Based Application on Adaptive Cross-Platform Decoder for Bit Patterned Magnetic Recording

2020

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Dynamic bit encoding and decoding of the magnetic recording process remain a challenge in that the process is restrained by the balance between reading and writing performance of the decoder's bit error rate (BER). Sequential neural networks offer data streamflow for processes to reproduce recoded bits from signal distribution, overcoming the limitation of codeword mapping designed for each specific bitpatterned magnetic recording (BPMR) channel. Here, we implement the vanilla long short-term memory (LSTM) for adaptive modulation decoders in various BPMR channel designs within a single network, which benefits multi-channel decoder calibration tools with the same standardization. Signal information from media readback, a two-dimensional (2D) equalizer, 2D Viterbi, and a 2D soft-output Viterbi algorithm (SOVA) detector is arranged as a tensor that enables sequence-to-sequence bit prediction even with a highly complex data arrangement. Our adaptive model can predict recorded bits from readback with accuracies of approximately 97% for rate 4/5 decoding and 75% for crossing platforms, using a recently proposed singlereader/two-track reading (SRTR) system at an areal density of 4 Tb/in 2 in a signal-to-noise ratio range of 1 to 8 dB. We conducted a BER simulation with the relevant results from conventional decoders and the LSTM model. Ultimately, our approach may demonstrate the limitation of supervised learning designed for BPMR systems and reveal a sequence data focus on LSTM that paves the way for sequential-type, unsupervised, mechanism-based, next-generation magnetic recordings.INDEX TERMS Long-short term memory (LSTM), supervised learning, deep learning, bit-patterned media recording (BPMR), channel decoding

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Section: A Ablation Studies Related To Bpmr Systemssupporting

confidence: 67%

Long-Short Term Memory-Based Application on Adaptive Cross-Platform Decoder for Bit Patterned Magnetic Recording

2020

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“…For example, social attention [39] models each node with the location of the agent, and edge with the distance between pedestrians, where the spatial relation is modeled with an attention module and then the temporal with RNNs. Similarly, [40] constructs the STGNN with Edge RNN and Node RNN based on the location; STGAT [19] uses GAT to capture the spatial interaction by assigning different importance to neighbors and adopts extra LSTMs to capture the temporal information of each agent. The major limitation of these methods is the difficulty in capturing the spatial interaction along the temporal dimension.…”

Section: Spatial-temporal Graph Network For Trajectory Predictionmentioning

confidence: 99%

GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction

Wang

Cai

Tan

2021

2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Trajectory prediction is a fundamental and challenging task to forecast the future path of the agents in autonomous applications with multi-agent interaction, where the agents need to predict the future movements of their neighbors to avoid collisions. To respond timely and precisely to the environment, high efficiency and accuracy are required in the prediction. Conventional approaches, e.g., LSTM-based models, take considerable computation costs in the prediction, especially for the long sequence prediction. To support more efficient and accurate trajectory predictions, we instead propose a novel CNN-based spatial-temporal graph framework GraphTCN, which captures the spatial and temporal interactions in an input-aware manner. The spatial interaction between agents at each time step is captured with an edge graph attention network (EGAT), and the temporal interaction across time step is modeled with a modified gated convolutional network (CNN). In contrast to conventional models, both the spatial and temporal modeling in GraphTCN are computed within each local time window. Therefore, GraphTCN can be executed in parallel for much higher efficiency, and meanwhile with accuracy comparable to best-performing approaches. Experimental results confirm that GraphTCN achieves noticeably better performance in terms of both efficiency and accuracy compared with state-of-the-art methods on various trajectory prediction benchmark datasets.

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“…Shen et al proposed employ the GNN in the person re-identification task that considers other nodes' information as part of similarity estimation [38]. Wang et al noticed that GNN structure can be used in the area of pedestrian trajectory prediction with an unsupervised manner [39]. Mohamed et al proposed that the GCN can be used as a interactive modelling tool in pedestrian trajectory prediction [40].…”

Section: Related Workmentioning

confidence: 99%

“…Wang et al . noticed that GNN structure can be used in the area of pedestrian trajectory prediction with an unsupervised manner [39]. Mohamed et al .…”

Section: Related Workmentioning

confidence: 99%

Contrastive learning of graph encoder for accelerating pedestrian trajectory prediction training

Yao

Ding

2021

IET Image Processing

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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.

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Unsupervised Pedestrian Trajectory Prediction with Graph Neural Networks

Cited by 13 publications

References 18 publications

Long-Short Term Memory-Based Application on Adaptive Cross-Platform Decoder for Bit Patterned Magnetic Recording

Long-Short Term Memory-Based Application on Adaptive Cross-Platform Decoder for Bit Patterned Magnetic Recording

GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction

Contrastive learning of graph encoder for accelerating pedestrian trajectory prediction training

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