Papers for

sports video editors

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

System tracks individual windsurfers in long-distance videos reliably

Identity-Consistent Analysis of Long-Shot Windsurfing Video: A Domain-Specific Offline Tracking System

Abstract: Long-shot windsurfing video combines small targets, large camera pans, prolonged overlaps, and rapidly changing backgrounds. The desired output is not a generic MOT trace but a separate, stable rider-relative video for each surfer; one false identity merge can invalidate an otherwise useful result. We present an offline analysis system that detects surfers, forms conservative local tracklets, links them globally with camera-compensated motion and a foreground-masked sail-color descriptor, and uses two pose keypoints on the rig to drive a rider-relative virtual camera. The tracking stage is evaluated on 21 manually reconstructed development videos containing 41,004 retained observations. On this fixed-observation protocol, the production system achieves 0.957 pairwise precision, 0.918 recall, and 0.937 F1, compared with 0.792 F1 for OC-SORT and 0.828 for BoT-SORT. Compared with OC-SORT, it reduces fragmentation excess from 845 to 42, but nine of its 95 output tracks mix rider identities and these errors affect seven of the 21 videos.

Mon 21 SeptComputer Vision and Pattern Recognition
The gist
Videos of windsurfing often show surfers from far away, making it hard to keep track of who is who because they are small and moving quickly with changing backgrounds. The authors created a system that carefully finds surfers and follows each one separately using details like sail color and motion. This system produces clearer and more accurate videos centered on each surfer compared to other tracking tools. Their approach reduces mistakes where identities mix up, although some errors still remain.
Open → 2609.24492v1

Boundary voting network improves action segmentation in videos

Boundary Voting Network for Ambiguity-Aware Timestamp-Supervised Action Segmentation

Abstract: Timestamp-supervised action segmentation aims to segment and classify actions in untrimmed videos with a random frame annotated per action. Precisely localizing action boundaries from timestamp annotations is crucial for this setting, as it enables generating framewise pseudo-labels and applying the well-explored fully-supervised training. However, prevailing methods struggle with intrinsic uncertainty in boundary localization due to less discriminative features in action-transiting regions. This imprecise boundary estimation significantly reduces the stability and reliability of the generated pseudo-labels in ambiguous action-transiting regions, consequently resulting in performance deterioration of the trained segmentation models. In our paper, we introduce the boundary voting network that mitigates feature ambiguity by hierarchically propagating video-level global prior knowledge into local action-transiting regions. By generating key action representations as votes throughout the video and targeting action-transiting regions, all votes collaboratively contribute to action-transiting feature enhancement and boundary localization refinement. Extensive experiments demonstrate the effectiveness of our method on GTEA, 50Salads, and Breakfast datasets.

Tue 8 SeptComputer Vision and Pattern Recognition
The gist
Videos often show activities that need to be divided into clear steps, but marking exactly where one step ends and another begins is tricky when only a few frames are labeled. The authors designed a method called boundary voting network to better locate these boundaries by using clues from the whole video, helping the computer guess more accurately. This new approach helps the model learn from uncertain regions and improves how well actions are segmented. They tested it on several video datasets and found it works better than previous methods.
Open → 2609.08167v1