The Library
Multiple object forecasting : predicting future object locations in diverse environments
Tools
Styles, Olly, Tanaya, Guha and Sanchez Silva, Victor (2020) Multiple object forecasting : predicting future object locations in diverse environments. In: IEEE Winter Conference on Applications of Computer Vision (WACV’20), Aspen, Colorado, 1-5 Mar 2020. Published in: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) ISBN 9781728165547. doi:10.1109/WACV45572.2020.9093446
|
PDF
WRAP-multiple-object-forecasting-predicting-future-object-locations-diverse-environments-Styles-2019.pdf - Accepted Version - Requires a PDF viewer. Download (2648Kb) | Preview |
Official URL: https://doi.org/10.1109/WACV45572.2020.9093446
Abstract
This paper introduces the problem of multiple object forecasting (MOF), in which the goal is to predict future bounding boxes of tracked objects. In contrast to existing works on object trajectory forecasting which primarily consider the problem from a birds-eye perspective, we formulate the problem from an object-level perspective and call for the prediction of full object bounding boxes, rather than trajectories alone. Towards solving this task, we introduce the Citywalks dataset, which consists of over 200k high-resolution video frames. Citywalks comprises of footage recorded in 21 cities from 10 European countries in a variety of weather conditions and over 3.5k unique pedestrian trajectories. For evaluation, we adapt existing trajectory forecasting methods for MOF and confirm cross-dataset generalizability on the MOT-17 dataset without fine-tuning. Finally, we present STED, a novel encoder-decoder architecture for MOF. STED combines visual and temporal features to model both object-motion and ego-motion, and outperforms existing approaches for MOF. Code & dataset link: this https URL
Item Type: | Conference Item (Paper) | ||||||
---|---|---|---|---|---|---|---|
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Journal or Publication Title: | 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) | ||||||
Publisher: | IEEE Computer Society | ||||||
ISBN: | 9781728165547 | ||||||
Official Date: | 14 May 2020 | ||||||
Dates: |
|
||||||
DOI: | 10.1109/WACV45572.2020.9093446 | ||||||
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: | 10 October 2019 | ||||||
Date of first compliant Open Access: | 13 January 2020 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | IEEE Winter Conference on Applications of Computer Vision (WACV’20) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Aspen, Colorado | ||||||
Date(s) of Event: | 1-5 Mar 2020 | ||||||
Related URLs: | |||||||
Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year