Understanding Video Content: Efficient Hero Detection and Recognition for the Game "Honor of Kings"
This work addresses the need for automated video content analysis in the specific domain of gaming, particularly for 'Honor of Kings', but it is incremental as it applies existing methods like template-matching and deep neural networks to a new dataset.
The paper tackled the problem of automatically detecting and recognizing characters (heroes) and their camps in videos of the game 'Honor of Kings' to understand video content and extract labels. The result was an efficient two-stage algorithm that achieved high accuracy in this task, though no specific numbers were provided.
In order to understand content and automatically extract labels for videos of the game "Honor of Kings", it is necessary to detect and recognize characters (called "hero") together with their camps in the game video. In this paper, we propose an efficient two-stage algorithm to detect and recognize heros in game videos. First, we detect all heros in a video frame based on blood bar template-matching method, and classify them according to their camps (self/ friend/ enemy). Then we recognize the name of each hero using one or more deep convolution neural networks. Our method needs almost no work for labelling training and testing samples in the recognition stage. Experiments show its efficiency and accuracy in the task of hero detection and recognition in game videos.