Understanding Video Content: Efficient Hero Detection and Recognition for the Game "Honor of Kings"

TL;DR

This paper presents an efficient two-stage method for detecting and recognizing heroes in 'Honor of Kings' game videos, combining template matching and deep learning to achieve high accuracy with minimal labeling effort.

Contribution

The paper introduces a novel two-stage approach that simplifies hero detection and recognition in game videos, reducing labeling requirements and improving efficiency.

Findings

High detection and recognition accuracy demonstrated

Method requires minimal labeling effort

Effective in real game video scenarios

Abstract

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.