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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, 16-18 Jun 2020. Published in: Conference on Computer Vision and Pattern Recognition (In Press)
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WRAP-Multi-camera-trajectory-forecasting-pedestrian-network-cameras-Styles-2020.pdf - Accepted Version - Requires a PDF viewer. Download (2217Kb) | Preview |
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) | |||||||||
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| 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 |
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| Divisions: | Faculty of 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: | Conference on Computer Vision and Pattern Recognition | |||||||||
| Publisher: | IEEE | |||||||||
| Official Date: | 2020 | |||||||||
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| Date of first compliant deposit: | 28 April 2020 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | In Press | |||||||||
| Publisher Statement: | © 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 | |||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | IEEE 2020 Conference on Computer Vision and Pattern Recognition | |||||||||
| Type of Event: | Workshop | |||||||||
| Location of Event: | Seattle | |||||||||
| Date(s) of Event: | 16-18 Jun 2020 | |||||||||
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