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A Latent Variable Model State Estimation System for Image Sequences

Kornfeld, Nils and Feng, Zachary (2019) A Latent Variable Model State Estimation System for Image Sequences. In: 22nd International Conference on Information Fusion. 22nd International Conference on Information Fusion, 2.-5. Juli 2019, Ottawa, Kanada.

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Official URL: https://fusion2019.org/

Abstract

Self-driving cars need to be able to assess and understand the state of their surroundings. To achieve this goal, it is necessary to construct a model, which holds information about the state of the environment, based on sensor measurements. In common state estimation systems like Kalman filters, it is necessary to explicitly model state transitions and the Observation process. These models have to match the internal dynamics of the observed system as closely as possible to yield reliable estimation results. In this work, we propose a method that can learn an approximation of the internal dynamics of a system, without the need to explicitly model these processes. Our system even works on highly complex data like frames of a video sequence. The approach is based on a latent variable model with a continuous hidden state space. To deal with the fact that the estimated processes are sequential, we use recurrent neural networks. As an example to show the potential of this system, resulting predicted future frames of short video sequences are shown. The proposed system shows a general approach for state estimation without the need for any knowledge about the underlying state transition or observation processes.

Item URL in elib:https://elib.dlr.de/127325/
Document Type:Conference or Workshop Item (Speech)
Title:A Latent Variable Model State Estimation System for Image Sequences
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Kornfeld, Nilsnils.kornfeld (at) dlr.deUNSPECIFIED
Feng, Zacharyzachary.feng (at) mail.mcgill.caUNSPECIFIED
Date:July 2019
Journal or Publication Title:22nd International Conference on Information Fusion
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:artificial intelligence, Image prediction, predictive models, predictive Encoding, artificial neural networks
Event Title:22nd International Conference on Information Fusion
Event Location:Ottawa, Kanada
Event Type:international Conference
Event Dates:2.-5. Juli 2019
Organizer:ISIF - International Society of Information Fusion
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - D.MoVe
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems > Data Management and Knowledge Discovery
Deposited By: Kornfeld, Nils
Deposited On:15 May 2019 10:34
Last Modified:08 Jan 2020 14:26

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