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A single-shot approach using an LSTM for moving object path prediction

Fernandez, Jaime B. orcid logoORCID: 0000-0001-9774-3879, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2019) A single-shot approach using an LSTM for moving object path prediction. In: Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), 6-9 Nov 2019, Istanbul, Turkey. ISBN 978-1-7281-3975-3

Abstract
This work presents an analysis of predicting the future path of moving objects from a moving camera on traffic scenes with an LSTM architecture in a single-shot manner. Path prediction allows us to estimate the future locations of an object in a given space and is useful in important applications such as surveillance, abnormal behaviour detection, crowd behaviour analysis, traffic control and currently in driver assistance (ADAS) or collision avoidance systems. Normal approaches use the last tobs positions of an object observed in video frames to predict its future path as a sequence of position values. This can then be treated as a time series. LSTM architectures are known for reaching good performance when dealing with time series. We evaluate path prediction across three types of objects (pedestrians, vehicles and cyclists), four prediction horizons (5,10, 15 and 20 frames ahead) and two different perspectives (image coordinate and birds-eye view). The approach described in this work reached an Average Displacement Error (ADE) of 0.01m for pedestrians, 0.06m for vehicles and 0.02m for cyclists and an average Final Displacement Error (FDE) of between 0.016m and 0.15m for near-future prediction using an LSTM architecure with relative tracklet positioning.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Path prediction; traffic scenes; LSTM; time series
Subjects:Computer Science > Artificial intelligence
Computer Science > Image processing
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), Proceedings. . IEEE. ISBN 978-1-7281-3975-3
Publisher:IEEE
Official URL:http://dx.doi.org/10.1109/IPTA.2019.8936126
Copyright Information:© 2019 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:EU H2020 Project VI-DAS under grant number 690772, Insight Centre for Data Analytics funded by SFI, grant number SFI/12/RC/2289
ID Code:24158
Deposited On:21 Jan 2020 11:25 by Jaime Boanerjes Fernandez Roblero . Last Modified 23 Nov 2022 14:21
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