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Computer Optics, 2016, Volume 40, Issue 6, Pages 958–967 (Mi co349)  

IMAGE PROCESSING, PATTERN RECOGNITION

Reducing background false positives for face detection in surveillance feeds

A. E. Sergeeva, V. S. Konushina, A. S. Konushinb

a National Research University Higher School of Economics, Moscow, Russia
b Video Analysis Technologies LLC, Moscow, Russia

Abstract: This paper addresses a problem of false positive detection filtering in surveillance video streams. We propose two methods. The first one is based on automatic hard negative mining from a video stream, which is then used for fine-tuning of the baseline detector. The second one is the detector output filtering by analyzing the frequency of detection of visually similar samples. We demonstrate the proposed methods on cascade-based detectors, but they can be applied to any detector that can be trained in a reasonable amount of time. Experimental results show that the proposed methods improve both the precision and recall rate, as well as reducing the computational time by 47%.

Keywords: detectors, pattern recognition, image analysis, machine vision algorithms.

Funding Agency Grant Number
Russian Foundation for Basic Research 15-31-20596-мол_а_вед
The work was partially funded by RFBR, grant No. 15-31-20596


DOI: https://doi.org/10.18287/2412-6179-2016-40-6-958-967

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Full text: http://www.computeroptics.smr.ru/.../400624.html
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Received: 16.01.2016
Accepted:10.10.2016

Citation: A. E. Sergeev, V. S. Konushin, A. S. Konushin, “Reducing background false positives for face detection in surveillance feeds”, Computer Optics, 40:6 (2016), 958–967

Citation in format AMSBIB
\Bibitem{SerKonKon16}
\by A.~E.~Sergeev, V.~S.~Konushin, A.~S.~Konushin
\paper Reducing background false positives for face detection in surveillance feeds
\jour Computer Optics
\yr 2016
\vol 40
\issue 6
\pages 958--967
\mathnet{http://mi.mathnet.ru/co349}
\crossref{https://doi.org/10.18287/2412-6179-2016-40-6-958-967}


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