Multiplayer Online Tactical Game Player Action Recognition Video Dataset
#action recognition #video classification #behavior prediction #sports analysis #e-sports #action recognition
- 500 records
- 1.5G
- MP4
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
Currently, the e-sports industry is experiencing rapid growth, but it faces the challenge of efficiently analyzing player actions to enhance performance. Existing solutions often rely on traditional manual analysis and simple video playback tools, which are usually inefficient and struggle to capture subtle action changes. The goal of building this dataset is to support the development of advanced action recognition algorithms to improve the accuracy and efficiency of player performance analysis. Data collection is conducted using high-definition recording equipment in a professional e-sports competition environment to ensure stability and consistency of the recordings. Quality control includes multiple rounds of annotation processes, step-by-step consistency checks, and final review by experts in the e-sports field. The annotation team consists of a group of 30 individuals with computer vision and e-sports experience. Data preprocessing involves technical steps such as video segmentation, action cropping, and format conversion. Data storage is organized in MP4 format for large-scale parallel processing and analysis.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| duration | string | Duration |
| quality | string | Resolution |
| player_count | int | The number of players participating in the game in the video. |
| game_environment | string | A description of the game environment in the video, such as map name or scene description. |
| player_position | string | The initial position of each player in the video. |
| action_sequence | string | The main sequence of actions performed by the players in the video. |
| team_id | string | The unique identifier for the team to which each player belongs. |
| score | float | The score obtained by each player or team in the video. |
| game_outcome | string | The final result or outcome of the game in the video. |
| weapon_used | string | The main weapon or tool used by the players in the video. |
| health_status | string | The health status of each player at various points in the video, such as full health, half health, etc. |
| special_skills | string | Whether the players used special skills and their effects in the video. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What type of actions is this dataset primarily used to recognize?
- This dataset is primarily used to recognize actions of players in multiplayer online tactical games.
- How does this dataset aid in enhancing sports analysis?
- By analyzing the actions of game players, this dataset can enhance understanding and analysis of eSports players' performance.
- In which fields can this type of video dataset be applied?
- This kind of video dataset can be applied in fields like eSports, action recognition research, and AI training.
- What type of research can researchers conduct using this dataset?
- Researchers can use this dataset to train machine learning models to improve automatic recognition of complex actions.
- What is the significance of this dataset for AI development?
- This dataset helps enhance AI's ability to perceive and understand dynamic actions, serving as an important resource for advancing intelligent analysis.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Multiplayer Online Tactical Game Player Action Recognition Video Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/9fc1efdf1e006a28e308a177c78c87e3?dataset_scene_cate_type=9},
urldate={2026-02-04},
keywords={e-sports action recognition, sports video analysis, e-sports player action data},
version={1.0}
}Using this in research? Please cite us.
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