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Multi-camera trajectory forecasting : pedestrian trajectory prediction in a network of cameras

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Styles, Olly, Guha, Tanaya, Sanchez Silva, Victor and Kot, Alex (2020) Multi-camera trajectory forecasting : pedestrian trajectory prediction in a network of cameras. In: IEEE 2020 Conference on Computer Vision and Pattern Recognition, Seattle, 14-19 Jun 2020. Published in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ISBN 9781728193618. doi:10.1109/CVPRW50498.2020.00516

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Official URL: https://doi.org/10.1109/CVPRW50498.2020.00516

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Abstract

We introduce the task of multi-camera trajectory forecasting (MCTF), where the future trajectory of an object is predicted in a network of cameras. Prior works consider forecasting trajectories in a single camera view. Our work is the first to consider the challenging scenario of forecasting across multiple non-overlapping camera views. This has wide applicability in tasks such as re-identification and multi-target multi-camera tracking. To facilitate research in this new area, we release the Warwick-NTU Multi-camera Forecasting Database (WNMF), a unique dataset of multi-camera pedestrian trajectories from a network of 15 synchronized cameras. To accurately label this large dataset (600 hours of video footage), we also develop a semi-automated annotation method. An effective MCTF model should proactively anticipate where and when a person will re-appear in the camera network. In this paper, we consider the task of predicting the next camera a pedestrian will re-appear after leaving the view of another camera, and present several baseline approaches for this. The labeled database is available online https://github.com/olly-styles/Multi-Camera-Trajectory-Forecasting

Item Type: Conference Item (Paper)
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
H Social Sciences > HV Social pathology. Social and public welfare
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TR Photography
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Remote-sensing images, Computer vision, Template matching (Digital image processing), Images, Photographic, Video surveillance, Surveillance detection, Image transmission, Automatic picture transmission
Journal or Publication Title: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Publisher: IEEE
ISBN: 9781728193618
Official Date: 28 July 2020
Dates:
DateEvent
28 July 2020Published
17 April 2020Accepted
DOI: 10.1109/CVPRW50498.2020.00516
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2020 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
Date of first compliant deposit: 28 April 2020
Date of first compliant Open Access: 29 April 2020
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/L016400/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
690907Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661
Conference Paper Type: Paper
Title of Event: IEEE 2020 Conference on Computer Vision and Pattern Recognition
Type of Event: Conference
Location of Event: Seattle
Date(s) of Event: 14-19 Jun 2020
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