稳健性(进化)
计算机科学
视频跟踪
计算机视觉
人工智能
甲骨文公司
目标检测
实时计算
对象(语法)
模式识别(心理学)
软件工程
生物化学
基因
化学
作者
Xue‐Jun Xie,Ying Ding,Songqiang Chen,Jifeng Xuan
标识
DOI:10.1109/issre55969.2022.00046
摘要
Due to the wide use of visual perception techniques in safety-critical fields, existing studies have tested the robustness of the essential object detection systems in scenarios with different image content. However, the applications that perceive one video with multiple image frames, such as autonomous driving, usually further require the trajectories of objects. This is mainly realized by combining detecting objects and associating detected objects in frames using multiple object tracking (MOT) systems. Thus, it is also essential to test the robustness of MOT systems, particularly in their exclusive scenarios that involve variety beyond the static image content. In this paper, we propose a novel testing method with five new Metamorphic Relations to realize the robustness test for MOT systems in two typical categories of scenarios, i.e., the speed variety of tracked objects and temporary camera failures. Our method also properly addresses the oracle problem and the lack of test cases for some rare scenarios to make the test efficient and diverse. Finally, we use our method to test three typical MOT systems and effectively reveal numerous and diverse MOT errors. We also extensively discuss the performance of tested systems and summarize two typical scenes where they often misbehave.
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