Abstract
Today's video surveillance systems are increasingly equipped with video content analysis for a great variety of applications. However, reliability and robustness of video content analysis algorithms remain an issue. They have to be measured against ground truth data in order to quantify the performance and advancements of new algorithms. Therefore, a variety of measures have been proposed in the literature, but there has neither been a systematic overview nor an evaluation of measures for specific video analysis tasks yet. This paper provides a systematic review of measures and compares their effectiveness for specific aspects, such as segmentation, tracking, and event detection. Focus is drawn on details like normalization issues, robustness, and representativeness. A software framework is introduced for continuously evaluating and documenting the performance of video surveillance systems. Based on many years of experience, a new set of representative measures is proposed as a fundamental part of an evaluation framework.
Highlights
The installation of videosurveillance systems is driven by the need to protect privateproperties, and by crime prevention, detection, and prosecution, for terrorism in public places
The background has to be completely stationary or moving globally. All these assumptions are violated in many real world scenarios, the tedious generation of ground truth (GT) becomes redundant
#TPc refers to the number of objects types classified correctly. #FNc is the number of false negatives caused by classification shortcomings, for example, unknown class, #FNc,det refers to the number of false negatives, caused by object detection errors or by classification shortcomings
Summary
Reliability and robustness of video content analysis algorithms remain an issue. They have to be measured against ground truth data in order to quantify the performance and advancements of new algorithms. A variety of measures have been proposed in the literature, but there has neither been a systematic overview nor an evaluation of measures for specific video analysis tasks yet. A software framework is introduced for continuously evaluating and documenting the performance of video surveillance systems. Based on many years of experience, a new set of representative measures is proposed as a fundamental part of an evaluation framework
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