This article is cited in 2 papers

IMAGE PROCESSING, PATTERN RECOGNITION

Reducing background false positives for face detection in surveillance feeds

A. E. Sergeev^a, V. S. Konushin^a, A. S. Konushin<sup>b

a</sup> 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.

Received: 16.01.2016
Accepted: 10.10.2016

DOI: 10.18287/2412-6179-2016-40-6-958-967