P2ANet: A Large-Scale Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos

核心概念

Challenging dense action detection in table tennis videos requires specialized benchmarks like P2ANet.

摘要

The article introduces P2ANet, a benchmark dataset for dense action detection in table tennis videos. It discusses the challenges of recognizing and localizing fast-moving actions in sports videos, particularly in table tennis. The dataset consists of 2,721 video clips from professional matches, annotated with fine-grained action labels. Various action recognition and localization models are evaluated on P2ANet, highlighting the difficulty of achieving high accuracy due to the dense and fast nature of the actions. The article also details the dataset construction, annotation process, and the development of a specialized annotation toolbox for efficient labeling.

Structure:

1. Introduction to Video Analytics and the Importance of Action Recognition in Sports Videos
2. Dataset Construction and Annotation Process for P2ANet
3. Evaluation of Action Recognition and Localization Models on P2ANet
4. Challenges and Insights from the Benchmark Evaluation

統計資料

These models can only achieve 48% area under the AR-AN curve for localization and 82% top-one accuracy for recognition.
P2ANet dataset consists of 2,721 annotated 6-minute-long video clips, containing 139,075 labeled action segments, and lasts 272 hours in total.

引述

"While deep learning has been widely used for video analytics, dense action detection with fast-moving subjects from sports videos is still challenging."
"The results confirm that P2ANet is still a challenging task and can be used as a special benchmark for dense action detection from videos."

從以下內容提煉的關鍵洞見

P2ANet

by Jiang Bian,X... 於 arxiv.org 03-27-2024

https://arxiv.org/pdf/2207.12730.pdf

深入探究

How can the challenges faced in dense action detection in table tennis videos be applied to other sports or industries

The challenges faced in dense action detection in table tennis videos can be applied to other sports or industries that involve fast-paced and intricate movements. For example, in sports like badminton or squash, where players make quick and precise shots, dense action detection algorithms developed for table tennis can be adapted to analyze and track player movements. In industries like manufacturing or robotics, where high-speed and precise actions are crucial, the techniques used in dense action detection can be applied to monitor and optimize processes. The ability to accurately detect and analyze fast-moving actions can enhance performance analysis, improve efficiency, and ensure safety in various domains.

What potential biases or limitations could arise from using professional players for data annotation in sports videos

Using professional players for data annotation in sports videos may introduce potential biases or limitations. One limitation is the subjectivity of professional players in labeling actions, as their expertise and experience may lead to assumptions or interpretations that are not easily understood by non-experts. This could result in biased annotations that do not accurately represent the actions in the videos. Additionally, professional players may have specific preferences or styles that could influence their labeling decisions, leading to inconsistencies in the dataset. Moreover, the reliance on professional players for annotation may limit the diversity of perspectives and expertise in the labeling process, potentially overlooking important nuances or variations in the actions being annotated.

How might the development of specialized benchmarks like P2ANet impact the future of video analytics and action recognition technologies

The development of specialized benchmarks like P2ANet can have a significant impact on the future of video analytics and action recognition technologies. These benchmarks provide standardized datasets and evaluation metrics that enable researchers and developers to compare and benchmark their algorithms effectively. By focusing on specific domains like table tennis, P2ANet allows for in-depth analysis and optimization of algorithms for dense action detection, leading to advancements in video understanding and recognition technologies. Furthermore, specialized benchmarks like P2ANet can drive innovation and collaboration in the research community, fostering the development of more robust and accurate models for action detection in various applications. As these benchmarks become more widely adopted, they can set new standards for performance and push the boundaries of what is achievable in video analytics and action recognition.