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Multi-camera trajectory forecasting with trajectory tensors
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Styles, Olly, Guha, Tanaya and Sanchez Silva, Victor (2021) Multi-camera trajectory forecasting with trajectory tensors. IEEE Transactions on Pattern Analysis and Machine Intelligence . doi:10.1109/TPAMI.2021.3107958 (In Press)
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WRAP-multi-camera-trajectory-forecasting-trajectory-tensors-Sanchez-Silva-2021.pdf - Accepted Version - Requires a PDF viewer. Download (2040Kb) | Preview |
Official URL: https://doi.org/10.1109/TPAMI.2021.3107958
Abstract
We introduce the problem of multi-camera trajectory forecasting (MCTF), which involves predicting the trajectory of a moving object across a network of cameras. While multi-camera setups are widespread for applications such as surveillance and traffic monitoring, existing trajectory forecasting methods typically focus on single-camera trajectory forecasting (SCTF), limiting their use for such applications. Furthermore, using a single camera limits the field-of-view available, making long-term trajectory forecasting impossible. We address these shortcomings of SCTF by developing an MCTF framework that simultaneously uses all estimated relative object locations from several viewpoints and predicts the object's future location in all possible viewpoints. Our framework follows a Which-When-Where approach that predicts in which camera(s) the objects appear and when and where within the camera views they appear. To this end, we propose the concept of trajectory tensors: a new technique to encode trajectories across multiple camera views and the associated uncertainties. We develop several encoder-decoder MCTF models for trajectory tensors and present extensive experiments on our own database (comprising 600 hours of video data from 15 camera views) created particularly for the MCTF task. Results show that our trajectory tensor models outperform coordinate trajectory-based MCTF models and existing SCTF methods adapted for MCTF.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Trajectory optimization, Computer vision , Image processing -- Digital techniques , Artificial intelligence , Multimedia communications , User-centered system design , Pattern recognition systems, Multisensor data fusion | ||||||
| Journal or Publication Title: | IEEE Transactions on Pattern Analysis and Machine Intelligence | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 0162-8828 | ||||||
| Official Date: | 26 August 2021 | ||||||
| Dates: |
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| DOI: | 10.1109/TPAMI.2021.3107958 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| Publisher Statement: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| RIOXX Funder/Project Grant: |
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