Fine-Grain Annotation of Cricket Videos
Rahul Anand Sharma, Pramod Sankar K, CV Jawahar

TL;DR
This paper presents a weakly-supervised method for fine-grain temporal annotation of cricket videos using online commentaries, enabling detailed action retrieval without manual labeling.
Contribution
It introduces a two-stage approach leveraging commentary for scene segmentation and semantic annotation at a fine temporal scale, a novel contribution in sports video analysis.
Findings
Enables retrieval of short, specific actions from hours of cricket videos.
Produces large, automatically labeled datasets for machine learning.
Achieves fine-grained semantic annotation without manual effort.
Abstract
The recognition of human activities is one of the key problems in video understanding. Action recognition is challenging even for specific categories of videos, such as sports, that contain only a small set of actions. Interestingly, sports videos are accompanied by detailed commentaries available online, which could be used to perform action annotation in a weakly-supervised setting. For the specific case of Cricket videos, we address the challenge of temporal segmentation and annotation of ctions with semantic descriptions. Our solution consists of two stages. In the first stage, the video is segmented into "scenes", by utilizing the scene category information extracted from text-commentary. The second stage consists of classifying video-shots as well as the phrases in the textual description into various categories. The relevant phrases are then suitably mapped to the video-shots.…
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