Localization of Ice-Rink for Broadcast Hockey Videos

TL;DR

This paper presents a straightforward method for automatically localizing hockey ice-rinks in broadcast videos by combining shot segmentation, a ResNet18-based regressor, and smoothing techniques to reduce jittering, achieving accurate homography estimation.

Contribution

The work introduces a novel framework that integrates shot segmentation, deep learning regression, and smoothing to improve ice-rink localization in hockey videos.

Findings

Successfully localizes ice-rink in broadcast videos

Reduces projection jittering through smoothing

Achieves accurate homography estimation

Abstract

In this work, an automatic and simple framework for hockey ice-rink localization from broadcast videos is introduced. First, video is broken into video-shots by a hierarchical partitioning of the video frames, and thresholding based on their histograms. To localize the frames on the ice-rink model, a ResNet18-based regressor is implemented and trained, which regresses to four control points on the model in a frame-by-frame fashion. This leads to the projection jittering problem in the video. To overcome this, in the inference phase, the trajectory of the control points on the ice-rink model are smoothed, for all the consecutive frames of a given video-shot, by convolving a Hann window with the achieved coordinates. Finally, the smoothed homography matrix is computed by using the direct linear transform on the four pairs of corresponding points. A hockey dataset for training and testing the regressor is gathered. The results show success of this simple and comprehensive procedure for localizing the hockey ice-rink and addressing the problem of jittering without affecting the accuracy of homography estimation.