复杂监控视频中大规模废弃目标检测的鲁棒前景与丢弃分析

Quanfu Fan, Sharath Pankanti
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引用次数: 19

摘要

提出了一种具有低误报率和高检测精度的大规模废弃物体检测系统。我们的系统的鲁棒性很大程度上归功于我们开发的前景分析方法,该方法可以在具有挑战性的条件下(如照明变化,低纹理和低对比度以及杂乱的背景)有效地区分前景对象和背景。这大大消除了由照明变化引起的误报,同时更好地保留了真实的下降。我们进一步进行放弃分析,以较小的准确性成本(≤2%)减少更多的误报,包括与人有关的误报。我们在不同具有挑战性的场景中收集的两个大型数据集上展示了我们方法的有效性,并提供了详细的实验分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Foreground and Abandonment Analysis for Large-Scale Abandoned Object Detection in Complex Surveillance Videos
We present a robust system for large-scale abandoned object detection (AOD) with low false positive rates and good detection accuracy under complex realistic scenarios. The robustness of our system is largely attributed to an approach we develop for foreground analysis, which can effectively differentiate foreground objects from background under challenging conditions such as lighting changes, low textureness and low contrast as well as cluttered background. This significantly eliminates false positives caused by lighting changes while retaining true drops better. We further perform abandonment analysis to reduce more false positives including those related to people, at a small cost of accuracy (≤ 2%). We demonstrate the effectiveness of our approach on two large data sets collected in various challenging scenes, providing detailed analysis of experiments.
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