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
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%.
detectors, pattern recognition, image analysis, machine vision algorithms.
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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
\by A.~E.~Sergeev, V.~S.~Konushin, A.~S.~Konushin
\paper Reducing background false positives for face detection in surveillance feeds
\jour Computer Optics
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