基于定性稳定性分析的电子游戏中未知物体的视觉检测

Q2 Computer Science

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-04-01 DOI:10.1109/TCIAIG.2015.2506741

X. Ge, Jochen Renz, Peng Zhang

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引用次数: 12

摘要

目前许多用于物体检测的计算机视觉方法只能检测到事先学习过的物体。在本文中，我们提出了一种基于重力和已经检测到的物体的稳定性，使用定性稳定性分析来推断图像中某些区域是否存在未知物体的方法。我们的方法递归地搜索这些区域的未知对象，直到所有检测到的对象形成一个稳定的结构或没有新的对象可以识别。我们使用流行的电子游戏《愤怒的小鸟》来评估我们的方法。我们只从检测绿猪开始，并能够自动识别和检测所有400多个可用关卡中的所有重要游戏对象。所有物体都能被准确可靠地检测到。我们的方法可以应用于其他电子游戏，其中物体服从重力并受多边形约束。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Visual Detection of Unknown Objects in Video Games Using Qualitative Stability Analysis

Many current computer vision approaches for object detection can only detect objects that have been learned in advance. In this paper, we present a method that uses qualitative stability analysis to infer the existence of unknown objects in certain areas of the images based on gravity and stability of already detected objects. Our method recursively searches these areas for unknown objects until all detected objects form a stable structure or no new objects can be identified anymore. We evaluate our method using the popular video game Angry Birds. We only start with detecting the green pigs and are able to automatically identify and detect all essential game objects in all 400+ available levels. All objects can be accurately and reliably detected. Our method can be applied to other video games where objects obey gravity and are bound by polygons.

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来源期刊

IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

4.60

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.