Comparison of target detection algorithms using adaptive background models

Daniela Hall, J. Nascimento, P. Ribeiro, E. Andrade, Plinio Moreno, S. Pesnel, T. List, R. Emonet, R. Fisher, J. S. Victor, J. Crowley
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引用次数: 124

Abstract

This article compares the performance of target detectors based on adaptive background differencing on public benchmark data. Five state of the art methods are described. The performance is evaluated using state of the art measures with respect to ground truth. The original points are the comparison to hand labelled ground truth and the evaluation on a large database. The simpler methods LOTS and SGM are more appropriate to the particular task as MGM using a more complex background model.
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基于自适应背景模型的目标检测算法比较
本文在公共基准数据上比较了基于自适应背景差分的目标检测器的性能。描述了五种最先进的方法。性能是用最先进的测量方法来评估的。原始点是与手工标记的地面真值的比较和对大型数据库的评估。简单的方法LOTS和SGM更适合于MGM使用更复杂的背景模型的特定任务。
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