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Proceedings Paper

Evaluation schemes for video and image anomaly detection algorithms
Author(s): Shibin Parameswaran; Josh Harguess; Christopher Barngrover; Scott Shafer; Michael Reese
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Paper Abstract

Video anomaly detection is a critical research area in computer vision. It is a natural first step before applying object recognition algorithms. There are many algorithms that detect anomalies (outliers) in videos and images that have been introduced in recent years. However, these algorithms behave and perform differently based on differences in domains and tasks to which they are subjected. In order to better understand the strengths and weaknesses of outlier algorithms and their applicability in a particular domain/task of interest, it is important to measure and quantify their performance using appropriate evaluation metrics. There are many evaluation metrics that have been used in the literature such as precision curves, precision-recall curves, and receiver operating characteristic (ROC) curves. In order to construct these different metrics, it is also important to choose an appropriate evaluation scheme that decides when a proposed detection is considered a true or a false detection. Choosing the right evaluation metric and the right scheme is very critical since the choice can introduce positive or negative bias in the measuring criterion and may favor (or work against) a particular algorithm or task. In this paper, we review evaluation metrics and popular evaluation schemes that are used to measure the performance of anomaly detection algorithms on videos and imagery with one or more anomalies. We analyze the biases introduced by these by measuring the performance of an existing anomaly detection algorithm.

Paper Details

Date Published: 12 May 2016
PDF: 12 pages
Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440D (12 May 2016); doi: 10.1117/12.2224667
Show Author Affiliations
Shibin Parameswaran, Space and Naval Warfare Systems Ctr. Pacific (United States)
Josh Harguess, Space and Naval Warfare Systems Ctr. Pacific (United States)
Christopher Barngrover, Space and Naval Warfare Systems Ctr. Pacific (United States)
Scott Shafer, Space and Naval Warfare Systems Ctr. Pacific (United States)
Michael Reese, Space and Naval Warfare Systems Ctr. Pacific (United States)


Published in SPIE Proceedings Vol. 9844:
Automatic Target Recognition XXVI
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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