The Relationship Between Tracking Performance and Video Quality

Abstract

The push to automate processes is ever expanding, including the process of video imagery object tracking. The Motion Imagery Standards Board (MISB) has published the Video National Imagery Interpretability Rating Scale (VNIIRS) which provides a framework for video data quality. One purpose of VNIIRS is to assist analysts in searching motion imagery quickly and efficiently. This paper explores the problems that arise when tracking tasks are performed by tracking algorithms rather than by human analysts. The discussed obstacles emphasize a need for a new task-based scale that supplies a rating for the observability of video data, within the context of automated exploitation. The first section of this paper presents our development of classifiers and quantifications of critical motion imagery characteristics, extracted from various video data features. The second portion exhibits our findings on the relationships between these characteristics and object tracker performance. Conducting this evaluation and assessing the interpretability loss associated with the various video data characteristics enables an automated method of predicting tracker performance. These findings can be leveraged in the execution of tailored processing and analysis of incoming data streams as well as in the development of an updated video quality standard to be applied in data collection management.