Mode Normalization Enhanced Recurrent Model for Multi-Modal Semantic Trajectory Prediction

Shaojie ZHU; Lei ZHANG; Bailong LIU; Shumin CUI; Changxing SHAO; Yun LI

doi:10.1587/transinf.2019EDL8130

Mode Normalization Enhanced Recurrent Model for Multi-Modal Semantic Trajectory Prediction

Shaojie ZHU, Lei ZHANG, Bailong LIU, Shumin CUI, Changxing SHAO, Yun LI

Full Text Views

0

Cite this

Summary :

Multi-modal semantic trajectory prediction has become a new challenge due to the rapid growth of multi-modal semantic trajectories with text message. Traditional RNN trajectory prediction methods have the following problems to process multi-modal semantic trajectory. The distribution of multi-modal trajectory samples shifts gradually with training. It leads to difficult convergency and long training time. Moreover, each modal feature shifts in different directions, which produces multiple distributions of dataset. To solve the above problems, MNERM (Mode Normalization Enhanced Recurrent Model) for multi-modal semantic trajectory is proposed. MNERM embeds multiple modal features together and combines the LSTM network to capture long-term dependency of trajectory. In addition, it designs Mode Normalization mechanism to normalize samples with multiple means and variances, and each distribution normalized falls into the action area of the activation function, so as to improve the prediction efficiency while improving greatly the training speed. Experiments on real dataset show that, compared with SERM, MNERM reduces the sensitivity of learning rate, improves the training speed by 9.120 times, increases HR@1 by 0.03, and reduces the ADE by 120 meters.

Publication: IEICE TRANSACTIONS on Information Vol.E103-D No.1 pp.174-176

Publication Date: 2020/01/01

Publicized: 2019/10/04

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2019EDL8130

Type of Manuscript: LETTER

Category: Artificial Intelligence, Data Mining

Authors

Shaojie ZHU
  China University of Mining and Technology,Ministry of Education
Lei ZHANG
  China University of Mining and Technology,Ministry of Education
Bailong LIU
  China University of Mining and Technology,Ministry of Education
Shumin CUI
  China University of Mining and Technology,Ministry of Education
Changxing SHAO
  China University of Mining and Technology,Ministry of Education
Yun LI
  China University of Mining and Technology,Ministry of Education

Keyword

multi-modal, semantic trajectory, mode normalization

Cite this

Copy

Shaojie ZHU, Lei ZHANG, Bailong LIU, Shumin CUI, Changxing SHAO, Yun LI, "Mode Normalization Enhanced Recurrent Model for Multi-Modal Semantic Trajectory Prediction" in IEICE TRANSACTIONS on Information, vol. E103-D, no. 1, pp. 174-176, January 2020, doi: 10.1587/transinf.2019EDL8130.
Abstract: Multi-modal semantic trajectory prediction has become a new challenge due to the rapid growth of multi-modal semantic trajectories with text message. Traditional RNN trajectory prediction methods have the following problems to process multi-modal semantic trajectory. The distribution of multi-modal trajectory samples shifts gradually with training. It leads to difficult convergency and long training time. Moreover, each modal feature shifts in different directions, which produces multiple distributions of dataset. To solve the above problems, MNERM (Mode Normalization Enhanced Recurrent Model) for multi-modal semantic trajectory is proposed. MNERM embeds multiple modal features together and combines the LSTM network to capture long-term dependency of trajectory. In addition, it designs Mode Normalization mechanism to normalize samples with multiple means and variances, and each distribution normalized falls into the action area of the activation function, so as to improve the prediction efficiency while improving greatly the training speed. Experiments on real dataset show that, compared with SERM, MNERM reduces the sensitivity of learning rate, improves the training speed by 9.120 times, increases HR@1 by 0.03, and reduces the ADE by 120 meters.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8130/_p

Copy

@ARTICLE{e103-d_1_174,
author={Shaojie ZHU, Lei ZHANG, Bailong LIU, Shumin CUI, Changxing SHAO, Yun LI, },
journal={IEICE TRANSACTIONS on Information},
title={Mode Normalization Enhanced Recurrent Model for Multi-Modal Semantic Trajectory Prediction},
year={2020},
volume={E103-D},
number={1},
pages={174-176},
abstract={Multi-modal semantic trajectory prediction has become a new challenge due to the rapid growth of multi-modal semantic trajectories with text message. Traditional RNN trajectory prediction methods have the following problems to process multi-modal semantic trajectory. The distribution of multi-modal trajectory samples shifts gradually with training. It leads to difficult convergency and long training time. Moreover, each modal feature shifts in different directions, which produces multiple distributions of dataset. To solve the above problems, MNERM (Mode Normalization Enhanced Recurrent Model) for multi-modal semantic trajectory is proposed. MNERM embeds multiple modal features together and combines the LSTM network to capture long-term dependency of trajectory. In addition, it designs Mode Normalization mechanism to normalize samples with multiple means and variances, and each distribution normalized falls into the action area of the activation function, so as to improve the prediction efficiency while improving greatly the training speed. Experiments on real dataset show that, compared with SERM, MNERM reduces the sensitivity of learning rate, improves the training speed by 9.120 times, increases HR@1 by 0.03, and reduces the ADE by 120 meters.},
keywords={},
doi={10.1587/transinf.2019EDL8130},
ISSN={1745-1361},
month={January},}

Copy

TY - JOUR
TI - Mode Normalization Enhanced Recurrent Model for Multi-Modal Semantic Trajectory Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 174
EP - 176
AU - Shaojie ZHU
AU - Lei ZHANG
AU - Bailong LIU
AU - Shumin CUI
AU - Changxing SHAO
AU - Yun LI
PY - 2020
DO - 10.1587/transinf.2019EDL8130
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2020
AB - Multi-modal semantic trajectory prediction has become a new challenge due to the rapid growth of multi-modal semantic trajectories with text message. Traditional RNN trajectory prediction methods have the following problems to process multi-modal semantic trajectory. The distribution of multi-modal trajectory samples shifts gradually with training. It leads to difficult convergency and long training time. Moreover, each modal feature shifts in different directions, which produces multiple distributions of dataset. To solve the above problems, MNERM (Mode Normalization Enhanced Recurrent Model) for multi-modal semantic trajectory is proposed. MNERM embeds multiple modal features together and combines the LSTM network to capture long-term dependency of trajectory. In addition, it designs Mode Normalization mechanism to normalize samples with multiple means and variances, and each distribution normalized falls into the action area of the activation function, so as to improve the prediction efficiency while improving greatly the training speed. Experiments on real dataset show that, compared with SERM, MNERM reduces the sensitivity of learning rate, improves the training speed by 9.120 times, increases HR@1 by 0.03, and reduces the ADE by 120 meters.
ER -