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Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters

Drory, Ami; Zhu, Gao; Li, Hongdong; Hartley, Richard

Authors

Ami Drory

Gao Zhu

Hongdong Li

Richard Hartley



Abstract

This paper addresses the problem of automatic detection and tracking of slalom paddlers through a long sequence of sports broadcast images comprised of persistent view changes. In this context, the task of visual object tracking is particularly challenging due to frequent shot transitions (i.e. camera switches), which violate the fundamental spatial continuity assumption used by most of the state-of-the-art object tracking algorithms. The problem is further compounded by significant variations in object location, shape and appearance in typical sports scenarios where the athletes often move rapidly. To overcome these challenges, we propose a Periodically Prior Regularised Discriminative Correlation Filters (PPRDCF) framework, which exploits recent successful Discriminative Correlation Filters (DCF) with a periodic regularisation by a prior that constitutes a rich discriminative cascade classifier. The PPRDCF framework reduces the corruption of positive samples during online learning of the correlation filters by negative training samples. Our framework detects rapid shot transitions to reinitialise the tracker. It successfully recovers the tracker when the location, view or scale of the object changes or the tracker drifts from the object. The PPRDCF also provides the race context by detection of the ordered course obstacles and their spatial relations to the paddler. Our framework robustly outputs the evidence base pre-requisite to derived race kinematics for analysis of performance. Experiments are performed on task-specific dataset containing Canoe/Kayak Slalom race image sequences with successful results obtained.

Citation

Drory, A., Zhu, G., Li, H., & Hartley, R. (2017). Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters. Computer Vision and Image Understanding, 159, 116-127. https://doi.org/10.1016/j.cviu.2016.12.002

Journal Article Type Article
Acceptance Date Dec 5, 2016
Online Publication Date Sep 6, 2016
Publication Date 2017-06
Deposit Date Aug 22, 2019
Journal Computer Vision and Image Understanding
Print ISSN 1077-3142
Electronic ISSN 1090-235X
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 159
Pages 116-127
DOI https://doi.org/10.1016/j.cviu.2016.12.002
Public URL https://nottingham-repository.worktribe.com/output/2471325
Publisher URL https://www.sciencedirect.com/science/article/pii/S1077314216301990
Additional Information This article is maintained by: Elsevier; Article Title: Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters; Journal Title: Computer Vision and Image Understanding; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.cviu.2016.12.002; Content Type: article; Copyright: © 2016 Elsevier Inc. All rights reserved.